diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 07926e5..a156411 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -30,7 +30,7 @@ jobs:
     - name: Setup pixi
       uses: prefix-dev/setup-pixi@v0.9.3
       with:
-        pixi-version: v0.70.0
+        pixi-version: v0.76.0
         cache: true
         environments: docs
         activate-environment: false
diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
index 4a19b2b..c1500df 100644
--- a/.github/workflows/testing.yml
+++ b/.github/workflows/testing.yml
@@ -16,7 +16,7 @@ jobs:
       - name: Setup Pixi
         uses: prefix-dev/setup-pixi@v0.9.3
         with:
-          pixi-version: v0.70.0
+          pixi-version: v0.76.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: tests
@@ -39,7 +39,7 @@ jobs:
       - name: Setup Pixi
         uses: prefix-dev/setup-pixi@v0.9.3
         with:
-          pixi-version: v0.70.0
+          pixi-version: v0.76.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: tests
diff --git a/benchmark/plot.py b/benchmark/plot.py
index 308d0b4..6154697 100644
--- a/benchmark/plot.py
+++ b/benchmark/plot.py
@@ -1,3 +1,4 @@
+from datetime import datetime
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -5,6 +6,30 @@
 import pandas as pd
 
 
+def collect_csv_files(*paths: Path) -> list[Path]:
+    """Collect CSV files from paths or from the default data directory.
+
+    Args:
+        paths: CSV files or directories containing CSV files.
+
+    Returns:
+        List of CSV file paths.
+    """
+    default_data_folder = Path(__file__).parent / "data"
+    inputs = [Path(p) for p in paths] if paths else [default_data_folder]
+    csv_files: list[Path] = []
+    for input_path in inputs:
+        if input_path.is_dir():
+            csv_files.extend(sorted(input_path.glob("*.csv")))
+        elif input_path.is_file() and input_path.suffix.lower() == ".csv":
+            csv_files.append(input_path)
+        else:
+            raise ValueError(f"Expected a CSV file or directory, got: {input_path}")
+    if not csv_files:
+        raise ValueError("No CSV files found for plotting")
+    return csv_files
+
+
 def plot_fps_data(data_folder: Path):
     """Read all CSVs from the data folder and plot the latest gym and sim fps by device.
 
@@ -92,6 +117,67 @@ def plot_fps_data(data_folder: Path):
     print(f"Plot saved to {output_path}")
 
 
+def plot_splat_data(*paths: Path):
+    """Plot splat rendering throughput and save render.png.
+
+    Args:
+        paths: CSV files or directories containing CSV files. If empty, uses benchmark/data.
+    """
+    csv_files = collect_csv_files(*paths)
+    required = {"test_type", "n_worlds", "fps", "device"}
+    series: list[dict[str, str | pd.DataFrame]] = []
+
+    for csv_file in csv_files:
+        df = pd.read_csv(csv_file)
+        if not required.issubset(df.columns):
+            missing = sorted(required.difference(df.columns))
+            raise ValueError(f"CSV {csv_file} is missing required columns: {missing}")
+        splat = df[df["test_type"] == "splat"].copy()
+        if splat.empty:
+            continue
+        splat = splat.sort_values("n_worlds")
+        device = str(splat["device"].iloc[-1]).lower()
+        date_token = csv_file.stem.split("_")[-2]
+        date_label = datetime.strptime(date_token, "%Y%m%d").strftime("%d.%m.%y")
+        series.append({"device": device, "date": date_label, "df": splat})
+
+    if not series:
+        raise ValueError("No splat benchmark data found in provided CSV files")
+
+    colors = {"cpu": "#0000AA", "gpu": "#76B900"}
+    device_names = {"cpu": "Intel Core i9-13900KF", "gpu": "NVIDIA RTX 4090"}
+    fig, ax = plt.subplots(1, 1, figsize=(7, 5))
+    fig.suptitle("Crazyflow Splat Rendering", fontsize=16, fontweight="bold", y=0.98)
+
+    for item in series:
+        device = str(item["device"])
+        date_label = str(item["date"])
+        df = item["df"]
+        label = f"{device_names.get(device, device.upper())} ({date_label})"
+        ax.plot(
+            df["n_worlds"],
+            df["fps"],
+            marker="o",
+            linestyle="-",
+            color=colors.get(device),
+            label=label,
+        )
+
+    ax.set_title("Images per second: Splat renderer")
+    ax.set_xlabel("Number of Worlds")
+    ax.set_xscale("log")
+    ax.set_yscale("log")
+    ax.grid(True)
+    ax.legend(loc="upper left")
+    format_log_axes(ax, {f"splat_{idx}": item["df"] for idx, item in enumerate(series)}, "splat_")
+    plt.tight_layout()
+
+    output_dir = csv_files[0].parent
+    output_path = output_dir / "render.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    print(f"Plot saved to {output_path}")
+
+
 def format_log_axes(ax: plt.Axes, dfs: dict[str, pd.DataFrame], prefix: str):
     """Format logarithmic axes with nice labels.
 
@@ -136,4 +222,6 @@ def format_log_axes(ax: plt.Axes, dfs: dict[str, pd.DataFrame], prefix: str):
 
 
 if __name__ == "__main__":
-    plot_fps_data(Path(__file__).parent / "data")
+    data_folder = Path(__file__).parent / "data"
+    plot_fps_data(data_folder)
+    plot_splat_data(data_folder)
diff --git a/benchmark/splat.py b/benchmark/splat.py
new file mode 100644
index 0000000..2e130b6
--- /dev/null
+++ b/benchmark/splat.py
@@ -0,0 +1,163 @@
+"""Benchmark gaussian splat rendering throughput inside a jax.lax.scan rollout.
+
+Loads a splat scene and a drone splat, then renders RGB images from the drone camera for a fixed
+number of frames inside a single scanned rollout, doubling the number of parallel worlds each run
+(1, 2, 4, 8, ...). The whole rollout is jitted, so the loop runs entirely on device and the frame
+count reported is the number of images XLA actually rasterizes.
+
+Requires splax and a CUDA-capable GPU because the splat camera sensor uses splax's GPU rasterizer.
+
+Run with::
+
+    pixi run -e benchmark python benchmark/splat.py --resolution "(64,64)" --n_frames 100
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import TYPE_CHECKING, Callable
+
+# splax rasterizes with warp, which needs GPU memory outside JAX's pool. Disable JAX preallocation
+# before it initializes so both share the device. Must run before the first jax import.
+os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+
+import fire
+import jax
+import jax.numpy as jnp
+import numpy as np
+from jax.errors import JaxRuntimeError
+from splax.io import fetch
+
+from crazyflow.sim import Sim
+from crazyflow.sim.sensors.splat import build_render_splat_fn
+from crazyflow.sim.splat import attach_splats
+
+if TYPE_CHECKING:
+    from jax import Array
+
+    from crazyflow.sim.data import SimData
+
+ASSETS_URL = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+
+
+def build_rollout(
+    sim: Sim, resolution: tuple[int, int], n_frames: int, steps_per_frame: int
+) -> Callable[[SimData], Array]:
+    """Build a jitted rollout that steps the sim and renders one image per frame.
+
+    Each frame advances the simulation, rasterizes the splats from the first drone's camera for
+    every world, and reduces the image to a scalar sum. Reducing inside the loop keeps XLA from
+    eliminating the render as dead code while avoiding materializing the full
+    (n_frames, n_worlds, H, W, 3) stack.
+    """
+    step_fn = sim.build_step_fn()
+    render = build_render_splat_fn(sim, drones=0, resolution=resolution)
+
+    @jax.jit
+    def rollout(data: SimData) -> Array:
+        def frame(data: SimData, _: None) -> tuple[SimData, Array]:
+            data = step_fn(data, n_steps=steps_per_frame)
+            return data, render(data).sum()
+
+        data, sums = jax.lax.scan(frame, data, length=n_frames)
+        return sums.sum()
+
+    return rollout
+
+
+def benchmark(
+    resolution: tuple[int, int] = (64, 64),
+    n_frames: int = 100,
+    max_worlds_exp: int = 12,
+    fps: int = 30,
+    n_repeats: int = 3,
+    scene_ply: str = "robot_hall.ply",
+    drone_ply: str = "cf21B_500.ply",
+):
+    """Benchmark splat rendering throughput for a growing number of parallel worlds.
+
+    Args:
+        resolution: Rendered image resolution as (width, height).
+        n_frames: Number of frames rendered per scanned rollout.
+        max_worlds_exp: Largest world count is ``2 ** max_worlds_exp``.
+        fps: Camera frame rate. Determines the physics steps taken between frames.
+        n_repeats: Number of timed rollouts per world count. The fastest run is reported.
+        scene_ply: Scene splat file name on the assets host.
+        drone_ply: Drone splat file name on the assets host.
+    """
+    logging.info("Fetching splat assets...")
+    scene = fetch(f"{ASSETS_URL}/{scene_ply}")
+    drone = fetch(f"{ASSETS_URL}/{drone_ply}")
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    csv_file = Path(__file__).parent / "data" / f"benchmark_results_{timestamp}.csv"
+    csv_file.parent.mkdir(exist_ok=True)
+    with open(csv_file, "w", newline="") as f:
+        f.write(
+            "test_type,n_drones,n_worlds,n_steps,total_time_s,avg_step_time_s,"
+            "fps,real_time_factor,device\n"
+        )
+
+    print(
+        f"\nSplat rendering benchmark, resolution {resolution[0]}x{resolution[1]}, {n_frames} "
+        f"frames per rollout"
+    )
+    print("-" * 80)
+    print(f"{'n_worlds':>10} {'rollout_s':>12} {'frame_ms':>12} {'fps':>14}")
+    print("-" * 80)
+
+    for n_worlds in [2**i for i in range(max_worlds_exp + 1)]:
+        try:
+            sim = Sim(n_worlds=n_worlds, control="state", device="gpu")
+            attach_splats(sim, scene=scene, drone=drone)
+            steps_per_frame = max(1, sim.freq // fps)
+
+            # Hold a constant target so the drone keeps moving and each frame renders a distinct
+            # pose. A static scene would let XLA hoist the render out of the loop.
+            cmd = np.zeros((sim.n_worlds, sim.n_drones, 13), dtype=np.float32)
+            cmd[..., 2] = 0.5
+            sim.reset()
+            sim.state_control(jnp.asarray(cmd, device=sim.device))
+
+            rollout = build_rollout(sim, resolution, n_frames, steps_per_frame)
+
+            # Warmup triggers JIT compilation of the full rollout.
+            jax.block_until_ready(rollout(sim.data))
+
+            times = []
+            for _ in range(n_repeats):
+                tstart = time.perf_counter()
+                jax.block_until_ready(rollout(sim.data))
+                times.append(time.perf_counter() - tstart)
+
+            assert rollout._cache_size() == 1, "rollout must only be jitted once"
+
+            rollout_s = min(times)
+            frame_ms = rollout_s / n_frames * 1e3
+            images_per_s = n_frames * n_worlds / rollout_s
+            print(f"{n_worlds:>10} {rollout_s:>12.4f} {frame_ms:>12.4f} {images_per_s:>14.3e}")
+            sim.close()
+
+            real_time_factor = (n_frames / fps) * n_worlds / rollout_s
+            with open(csv_file, "a", newline="") as f:
+                f.write(
+                    f"splat,{sim.n_drones},{n_worlds},{n_frames},{rollout_s},"
+                    f"{rollout_s / n_frames},{images_per_s},{real_time_factor},gpu\n"
+                )
+        except (JaxRuntimeError, MemoryError) as e:
+            print(f"{n_worlds:>10}   out of memory, stopping ({type(e).__name__})")
+            break
+
+    print("-" * 80)
+    print("fps is total images rendered per second across all parallel worlds.\n")
+    print(f"Benchmark results saved to {csv_file}")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("jax").setLevel(logging.WARNING)
+    fire.Fire(benchmark)
diff --git a/crazyflow/sim/sensors/__init__.py b/crazyflow/sim/sensors/__init__.py
new file mode 100644
index 0000000..730b49f
--- /dev/null
+++ b/crazyflow/sim/sensors/__init__.py
@@ -0,0 +1,6 @@
+"""Sensors for the simulation.
+
+:mod:`crazyflow.sim.sensors.depth` renders depth images with MuJoCo raycasting and is always
+available. :mod:`crazyflow.sim.sensors.splat` renders photorealistic RGB(-D) images from gaussian
+splats and requires the optional ``splats`` extra.
+"""
diff --git a/crazyflow/sim/sensors.py b/crazyflow/sim/sensors/depth.py
similarity index 100%
rename from crazyflow/sim/sensors.py
rename to crazyflow/sim/sensors/depth.py
diff --git a/crazyflow/sim/sensors/splat.py b/crazyflow/sim/sensors/splat.py
new file mode 100644
index 0000000..d898d1c
--- /dev/null
+++ b/crazyflow/sim/sensors/splat.py
@@ -0,0 +1,374 @@
+"""Gaussian splat camera sensors built on `splax <https://github.com/learnsyslab/splax>`_.
+
+Renders batched RGB(-D) images of the splats attached via :func:`crazyflow.sim.splat.attach_splats`
+from any model camera. splax rasterizes with Warp kernels only, so this module requires the
+``splats`` extra and a simulation constructed with ``device="gpu"``.
+"""
+
+from __future__ import annotations
+
+from functools import partial
+from typing import TYPE_CHECKING
+
+import jax
+import jax.numpy as jnp
+import mujoco
+import numpy as np
+import splax
+from scipy.spatial.transform import Rotation as R
+
+from crazyflow.sim.sim import sync_sim2mjx
+from crazyflow.sim.splat import SPLAT_KEYS, SPLAT_SLICES_KEY, requires_gpu, requires_splats
+
+if TYPE_CHECKING:
+    from typing import Callable, Sequence
+
+    from jax import Array
+    from mujoco.mjx import Data, Model
+
+    from crazyflow.sim.data import SimData
+    from crazyflow.sim.sim import Sim
+
+
+@requires_splats
+@requires_gpu
+def render_splat_rgb(
+    sim: Sim,
+    drones: int | Sequence[int] | None = None,
+    resolution: tuple[int, int] = (640, 480),
+    background: tuple[float, float, float] = (1.0, 1.0, 1.0),
+    exclude_self: bool = False,
+    camera_prefix: str = "fpv_cam",
+) -> Array:
+    """Render RGB images of the attached splats in all worlds.
+
+    Scene gaussians are rendered at their static pose, drone gaussians follow the drone poses.
+
+    Args:
+        sim: The simulation to render.
+        drones: Drones whose cameras are rendered. ``None`` renders every drone, an int renders one,
+            and a sequence renders that subset in order.
+        resolution: Image resolution as (width, height).
+        background: RGB background color with values in [0, 1].
+        exclude_self: Hide each drone's own splat from its camera so a drone does not see itself.
+        camera_prefix: Camera name prefix, resolved to ``{camera_prefix}:{drone}`` for each drone.
+
+    Returns:
+        RGB images with values in [0, 1] of shape (n_worlds, n_selected, height, width, 3).
+    """
+    drone_ids = _resolve_drones(sim, drones)
+    camera_ids = tuple(_camera_id(sim.mj_model, camera_prefix, d) for d in drone_ids)
+    f, c = camera_intrinsics(sim.mj_model, camera_ids[0], resolution)
+    # tolist pulls the bounds over in one transfer, int() would sync the device per bound
+    slices = tuple(map(tuple, np.asarray(sim.data.plugins[SPLAT_SLICES_KEY]).tolist()))
+    return _render(
+        sim.data,
+        sim.mjx_data,
+        sim.mjx_model,
+        camera_ids=camera_ids,
+        slices=slices,
+        img_shape=(resolution[1], resolution[0]),
+        f=f,
+        c=c,
+        background=background,
+        exclude=drone_ids if exclude_self else None,
+    )
+
+
+@requires_splats
+@requires_gpu
+def render_splat_rgbd(
+    sim: Sim,
+    drones: int | Sequence[int] | None = None,
+    resolution: tuple[int, int] = (640, 480),
+    background: tuple[float, float, float] = (1.0, 1.0, 1.0),
+    alpha_threshold: float = 0.5,
+    max_range: float = 10.0,
+    exclude_self: bool = False,
+    camera_prefix: str = "fpv_cam",
+) -> Array:
+    """Render RGB-D images of the attached splats in all worlds.
+
+    Adds the expected depth of the gaussians as a fourth channel. Pixels whose coverage stays below
+    ``alpha_threshold`` are read as empty space and report ``max_range``.
+
+    Args:
+        sim: The simulation to render.
+        drones: Drones whose cameras are rendered. ``None`` renders every drone, an int renders one,
+            and a sequence renders that subset in order.
+        resolution: Image resolution as (width, height).
+        background: RGB background color with values in [0, 1].
+        alpha_threshold: Accumulated coverage a pixel needs before its depth counts as a hit.
+        max_range: Sensor range in meters. Reported where nothing is hit, and depth clips to it.
+        exclude_self: Hide each drone's own splat from its camera so a drone does not see itself.
+        camera_prefix: Camera name prefix, resolved to ``{camera_prefix}:{drone}`` for each drone.
+
+    Returns:
+        RGB in [0, 1] followed by depth in meters along the camera's optical axis, of shape
+        (n_worlds, n_selected, height, width, 4).
+    """
+    drone_ids = _resolve_drones(sim, drones)
+    camera_ids = tuple(_camera_id(sim.mj_model, camera_prefix, d) for d in drone_ids)
+    f, c = camera_intrinsics(sim.mj_model, camera_ids[0], resolution)
+    # tolist pulls the bounds over in one transfer, int() would sync the device per bound
+    slices = tuple(map(tuple, np.asarray(sim.data.plugins[SPLAT_SLICES_KEY]).tolist()))
+    return _render(
+        sim.data,
+        sim.mjx_data,
+        sim.mjx_model,
+        camera_ids=camera_ids,
+        slices=slices,
+        img_shape=(resolution[1], resolution[0]),
+        f=f,
+        c=c,
+        background=background,
+        exclude=drone_ids if exclude_self else None,
+        depth=True,
+        alpha_threshold=alpha_threshold,
+        max_range=max_range,
+    )
+
+
+def _resolve_drones(sim: Sim, drones: int | Sequence[int] | None) -> tuple[int, ...]:
+    """Normalize a drone selection to a tuple of drone indices."""
+    if isinstance(drones, (int, np.integer)):
+        return (int(drones),)
+    ids = range(sim.n_drones) if drones is None else drones
+    return tuple(int(d) for d in ids)
+
+
+def _camera_id(mj_model: mujoco.MjModel, prefix: str, drone: int) -> int:
+    """Camera index of a drone for the given camera name prefix."""
+    name = f"{prefix}:{drone}"
+    camera_id = mujoco.mj_name2id(mj_model, mujoco.mjtObj.mjOBJ_CAMERA, name)
+    if camera_id < 0:
+        raise ValueError(f"Camera '{name}' not found in the model")
+    return camera_id
+
+
+@requires_splats
+@requires_gpu
+def build_render_splat_fn(
+    sim: Sim,
+    drones: int | Sequence[int] | None = None,
+    resolution: tuple[int, int] = (640, 480),
+    background: tuple[float, float, float] = (1.0, 1.0, 1.0),
+    exclude_self: bool = False,
+    camera_prefix: str = "fpv_cam",
+) -> Callable[[SimData], Array]:
+    """Build a splat renderer function for a given drone selection, camera prefix, and resolution.
+
+    We bake all arguments into the function to avoid the overhead of flattening static arguments,
+    significantly improving performance.
+    """
+    drone_ids = _resolve_drones(sim, drones)
+    cameras_ids = tuple(_camera_id(sim.mj_model, camera_prefix, d) for d in drone_ids)
+    f, c = camera_intrinsics(sim.mj_model, cameras_ids[0], resolution)
+    # tolist pulls the bounds over in one transfer, int() would sync the device per bound
+    slices = tuple(map(tuple, np.asarray(sim.data.plugins[SPLAT_SLICES_KEY]).tolist()))
+    return jax.jit(
+        partial(
+            _render,
+            mjx_data=sim.mjx_data,
+            mjx_model=sim.mjx_model,
+            camera_ids=cameras_ids,
+            slices=slices,
+            img_shape=(resolution[1], resolution[0]),
+            f=f,
+            c=c,
+            background=background,
+            exclude=drone_ids if exclude_self else None,
+        )
+    )
+
+
+@requires_splats
+@requires_gpu
+def build_render_splat_rgbd_fn(
+    sim: Sim,
+    drones: int | Sequence[int] | None = None,
+    resolution: tuple[int, int] = (640, 480),
+    background: tuple[float, float, float] = (1.0, 1.0, 1.0),
+    alpha_threshold: float = 0.5,
+    max_range: float = 10.0,
+    exclude_self: bool = False,
+    camera_prefix: str = "fpv_cam",
+) -> Callable[[SimData], Array]:
+    """Build a splat RGB-D renderer for a drone selection, camera prefix, and resolution.
+
+    Mirrors :func:`build_render_splat_fn`.
+    """
+    drone_ids = _resolve_drones(sim, drones)
+    camera_ids = tuple(_camera_id(sim.mj_model, camera_prefix, d) for d in drone_ids)
+    f, c = camera_intrinsics(sim.mj_model, camera_ids[0], resolution)
+    # tolist pulls the bounds over in one transfer, int() would sync the device per bound
+    slices = tuple(map(tuple, np.asarray(sim.data.plugins[SPLAT_SLICES_KEY]).tolist()))
+    # The outer jit turns the MuJoCo model and data into baked-in constants rather than arguments
+    # flattened on every call, which otherwise dominates the runtime of a single render.
+    return jax.jit(
+        partial(
+            _render,
+            mjx_data=sim.mjx_data,
+            mjx_model=sim.mjx_model,
+            camera_ids=camera_ids,
+            slices=slices,
+            img_shape=(resolution[1], resolution[0]),
+            f=f,
+            c=c,
+            background=background,
+            exclude=drone_ids if exclude_self else None,
+            depth=True,
+            alpha_threshold=alpha_threshold,
+            max_range=max_range,
+        )
+    )
+
+
+@jax.jit
+def viewmats(cam_xpos: Array, cam_xmat: Array) -> Array:
+    """World-to-camera matrices in OpenCV convention for MuJoCo cameras.
+
+    MuJoCo cameras look along -z with +y up (OpenGL convention), splax expects OpenCV convention
+    cameras (+z forward, +y down).
+
+    Args:
+        cam_xpos: Camera positions of shape (..., 3).
+        cam_xmat: Camera rotation matrices (camera-to-world) of shape (..., 3, 3).
+
+    Returns:
+        World-to-camera matrices of shape (..., 4, 4).
+    """
+    rot_c2w = cam_xmat * jnp.array([1.0, -1.0, -1.0])  # Flip the y and z camera axes (columns)
+    rot = jnp.swapaxes(rot_c2w, -1, -2)
+    return _homogeneous(rot, -(rot @ cam_xpos[..., None])[..., 0])
+
+
+# TODO: RigidTransform.from_components produces NaN gradients. Swap once scipy#25767 is released.
+def _homogeneous(rot: Array, trans: Array) -> Array:
+    """Stack a rotation matrix and a translation into a (..., 4, 4) homogeneous transform."""
+    bottom = jnp.broadcast_to(jnp.array([0.0, 0.0, 0.0, 1.0]), (*rot.shape[:-2], 1, 4))
+    return jnp.concatenate([jnp.concatenate([rot, trans[..., None]], -1), bottom], -2)
+
+
+def camera_intrinsics(
+    mj_model: mujoco.MjModel, camera_id: int, resolution: tuple[int, int]
+) -> tuple[tuple[float, float], tuple[float, float]]:
+    """Pinhole intrinsics of a model camera for a given image resolution.
+
+    Args:
+        mj_model: MuJoCo model containing the camera.
+        camera_id: Camera index.
+        resolution: Image resolution as (width, height).
+
+    Returns:
+        Focal lengths (fx, fy) and principal point (cx, cy) in pixels.
+    """
+    width, height = resolution
+    fov_y = np.deg2rad(mj_model.cam_fovy[camera_id])
+    focal = float(height / (2.0 * np.tan(fov_y / 2.0)))
+    return (focal, focal), (width / 2.0, height / 2.0)
+
+
+def _camera_transforms(
+    cam_xpos: Array,
+    cam_xmat: Array,
+    pos: Array,
+    quat: Array,
+    slices: tuple[tuple[int, int], ...],
+    exclude: tuple[int, ...] | None,
+) -> tuple[Array, Array | None, int | None]:
+    """View matrices, drone transforms, and the axis the transforms are vmapped over per camera."""
+    # viewmats needs a single leading batch axis, the render is then nested-vmapped over both axes.
+    vm = viewmats(cam_xpos.reshape(-1, 3), cam_xmat.reshape(-1, 3, 3))
+    vm = vm.reshape(*cam_xpos.shape[:2], 4, 4)
+    if not slices:
+        return vm, None, None
+    tfs = _homogeneous(R.from_quat(quat).as_matrix(), pos)  # (n_worlds, n_drones)
+    if exclude is None:
+        return vm, tfs, None
+    # Slices are static, so teleport each camera's own drone far below the scene to cull it.
+    far = jnp.eye(4, dtype=tfs.dtype).at[2, 3].set(-1e4)
+    n_cams = cam_xpos.shape[1]
+    tfs = jnp.broadcast_to(tfs[:, None], (tfs.shape[0], n_cams, *tfs.shape[1:]))
+    return vm, tfs.at[:, jnp.arange(n_cams), jnp.asarray(exclude)].set(far), 0
+
+
+@jax.jit(
+    static_argnames=(
+        "camera_ids",
+        "slices",
+        "img_shape",
+        "f",
+        "c",
+        "background",
+        "exclude",
+        "depth",
+        "alpha_threshold",
+        "max_range",
+    )
+)
+def _render(
+    data: SimData,
+    mjx_data: Data,
+    mjx_model: Model,
+    camera_ids: tuple[int, ...],
+    slices: tuple[tuple[int, int], ...],
+    img_shape: tuple[int, int],
+    f: tuple[float, float],
+    c: tuple[float, float],
+    background: tuple[float, float, float],
+    exclude: tuple[int, ...] | None,
+    depth: bool = False,
+    alpha_threshold: float = 0.5,
+    max_range: float = 10.0,
+) -> Array:
+    """Render the splats from every camera with each drone at its current pose.
+
+    Args:
+        data: Simulation data holding the drone states and the attached splats.
+        mjx_data: MuJoCo data the camera kinematics are computed from.
+        mjx_model: MuJoCo model the camera kinematics are computed from.
+        camera_ids: Model camera index rendered for each selected drone.
+        slices: Start and stop index of each drone's gaussians.
+        img_shape: Image size as (height, width).
+        f: Focal lengths in pixels.
+        c: Principal point in pixels.
+        background: RGB background color.
+        exclude: Drone culled from each camera, or None to render every drone everywhere.
+        depth: If True, append the depth in meters as a fourth channel.
+        alpha_threshold: Coverage a pixel needs before its depth counts as a hit. Unused for RGB.
+        max_range: Sensor range in meters. Unused for RGB.
+
+    Returns:
+        Images of shape (n_worlds, n_cams, height, width, 3), one channel deeper with ``depth``.
+    """
+    _, mjx_data = sync_sim2mjx(data, mjx_data, mjx_model)  # We need the current camera poses
+    arrays = tuple(data.plugins[key] for key in SPLAT_KEYS)
+    vm, tfs, cam_axis = _camera_transforms(
+        mjx_data.cam_xpos[:, camera_ids],
+        mjx_data.cam_xmat[:, camera_ids],
+        data.states.pos,
+        data.states.quat,
+        slices,
+        exclude,
+    )
+    render = partial(
+        splax.render,
+        *arrays,
+        background=jnp.asarray(background, dtype=arrays[0].dtype),
+        img_shape=img_shape,
+        f=f,
+        c=c,
+        render_depth=depth,
+    )
+    if tfs is None:
+        img, alpha = jax.vmap(jax.vmap(lambda v: render(viewmat=v)))(vm)
+    else:
+        render_cam = lambda v, t: render(viewmat=v, gaussian_transforms=t, gaussian_slices=slices)  # noqa: E731
+        img, alpha = jax.vmap(jax.vmap(render_cam, in_axes=(0, cam_axis)), in_axes=(0, 0))(vm, tfs)
+    if not depth:
+        return img
+    # splax reads 0 depth where nothing was hit, so thin coverage would otherwise come out as a
+    # surface right at the camera rather than as empty space.
+    metric = jnp.where(alpha < alpha_threshold, max_range, jnp.minimum(img[..., 3], max_range))
+    return img.at[..., 3].set(metric)
diff --git a/crazyflow/sim/sim.py b/crazyflow/sim/sim.py
index e2e3ab2..5ad57de 100644
--- a/crazyflow/sim/sim.py
+++ b/crazyflow/sim/sim.py
@@ -193,6 +193,20 @@ def render(
         width: int = 1920,
         height: int = 1080,
     ) -> NDArray | None:
+        """Render one world of the simulation.
+
+        Args:
+            mode: Render mode. ``"human"`` opens an interactive MuJoCo viewer window,
+                ``"rgb_array"``, ``"depth_array"`` and ``"rgbd_tuple"`` return offscreen renderings.
+            world: Index of the world to render.
+            camera: Camera name or id. -1 is the global free camera.
+            cam_config: Camera configuration for the MuJoCo viewer.
+            width: Image width for offscreen rendering.
+            height: Image height for offscreen rendering.
+
+        Returns:
+            The rendered image(s) for the offscreen modes, None for ``"human"``.
+        """
         if self.viewer is None:
             if isinstance(camera, str):
                 cam_id = mujoco.mj_name2id(self.mj_model, mujoco.mjtObj.mjOBJ_CAMERA, camera)
diff --git a/crazyflow/sim/splat.py b/crazyflow/sim/splat.py
new file mode 100644
index 0000000..55a8dec
--- /dev/null
+++ b/crazyflow/sim/splat.py
@@ -0,0 +1,160 @@
+"""Gaussian splat support built on `splax <https://github.com/learnsyslab/splax>`_.
+
+This module owns the splat plugin state and its visualization. :func:`attach_splats` loads 3D
+gaussian splatting ``.ply`` files and stores them in the simulation's plugin data, and
+:class:`SplatViewer` streams the splats to a web-based viewer.
+
+The batched RGB camera sensor that renders splats lives in :mod:`crazyflow.sim.sensors.splat`.
+
+Splat files must be aligned to the simulation frames. The viewer renders in the browser and works on
+any device. Camera sensors rasterize on the GPU only.
+"""
+
+from __future__ import annotations
+
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, ParamSpec, TypeVar
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import splax
+from splax.viewer import Viewer
+
+from crazyflow.sim.sim import requires_mujoco_sync
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+    from numpy.typing import NDArray
+
+    from crazyflow.sim.sim import Sim
+
+Params = ParamSpec("Params")
+Return = TypeVar("Return")
+
+SPLAT_KEYS = (
+    "splat_means",
+    "splat_log_scales",
+    "splat_quats",
+    "splat_sh_colors",
+    "splat_logit_opacities",
+)
+"""Keys under which the splat arrays are stored in ``sim.data.plugins``.
+
+The gaussians form a single buffer per array, laid out as ``[scene | drone:0 | ... | drone:n-1]``.
+Scene gaussians are not part of any slice and remain static. Each drone's slice follows that drone's
+world pose.
+"""
+
+SPLAT_SLICES_KEY = "splat_slices"
+"""Key of the (n_drones, 2) array of per-drone (start, stop) gaussian buffer slices."""
+
+
+def requires_splats(fn: Callable[Params, Return]) -> Callable[Params, Return]:
+    """Decorator to ensure that the simulation has splats attached."""
+
+    @wraps(fn)
+    def wrapper(sim: Sim, *args: Any, **kwargs: Any) -> Return:
+        if not all(k in sim.data.plugins for k in SPLAT_KEYS):
+            raise RuntimeError("No splats attached to this simulation, call attach_splats first")
+        return fn(sim, *args, **kwargs)
+
+    return wrapper
+
+
+def requires_gpu(fn: Callable[Params, Return]) -> Callable[Params, Return]:
+    """Decorator to ensure that the simulation is running on the GPU."""
+
+    @wraps(fn)
+    def wrapper(sim: Sim, *args: Any, **kwargs: Any) -> Return:
+        if sim.device.platform != "gpu":
+            raise RuntimeError("Gaussian splatting requires running on the GPU.")
+        return fn(sim, *args, **kwargs)
+
+    return wrapper
+
+
+def attach_splats(sim: Sim, scene: Path | None = None, drone: Path | None = None):
+    """Load gaussian splat ``.ply`` files and attach them to the simulation.
+
+    The drone splat is replicated once per drone so that each drone has its own slice of gaussians.
+    All splat state, including the drone slice metadata, is inserted into ``sim.data.plugins`` and
+    the default data is rebuilt so it survives resets.
+
+    Args:
+        sim: The simulation to attach the splats to.
+        scene: Path to a static scene splat, aligned to the MuJoCo world frame.
+        drone: Path to a drone splat in the drone body frame, replicated for each drone.
+    """
+    if scene is None and drone is None:
+        raise ValueError("At least one of scene or drone must be provided")
+    parts, n_splats, slices = [], 0, ()
+    if scene is not None:
+        scene_arrays = splax.io.load_ply(scene)
+        n_splats = scene_arrays[0].shape[0]
+        parts.append(scene_arrays)
+    if drone is not None:
+        drone_arrays = splax.io.load_ply(drone)
+        n_drone_splats = drone_arrays[0].shape[0]
+        parts.extend([drone_arrays] * sim.n_drones)
+        starts = [n_splats + i * n_drone_splats for i in range(sim.n_drones)]
+        slices = tuple((start, start + n_drone_splats) for start in starts)
+    arrays = [jnp.concatenate(x, axis=0) for x in zip(*parts)]
+    splat_data = dict(zip(SPLAT_KEYS, arrays))
+    splat_data[SPLAT_SLICES_KEY] = jnp.asarray(slices, dtype=jnp.int32).reshape(-1, 2)
+    splat_data = jax.device_put(splat_data, sim.device)
+    sim.data = sim.data.replace(plugins=sim.data.plugins | splat_data)
+    sim.build_default_data()
+
+
+class SplatViewer:
+    """Web-based gaussian splat viewer.
+
+    Starts a ``splax.viewer.Viewer`` (viser web server) and uploads all attached splats once.
+    :meth:`update` then only pushes the current drone poses, so the viewer runs at real-time rates
+    on any device. The viewer is owned by its creator and is independent of ``sim.render()``.
+
+    Args:
+        sim: The simulation to visualize. Requires :func:`attach_splats` to have been called.
+        port: Port of the web server.
+    """
+
+    def __init__(self, sim: Sim, port: int = 8080):
+        if not all(k in sim.data.plugins for k in SPLAT_KEYS):
+            raise RuntimeError("No splats attached to this simulation, call attach_splats first")
+        arrays = tuple(sim.data.plugins[key] for key in SPLAT_KEYS)
+        slices = tuple((start, stop) for start, stop in sim.data.plugins[SPLAT_SLICES_KEY])
+        self.viewer = Viewer(port=port)
+        n_splats = slices[0][0] if slices else arrays[0].shape[0]
+        if n_splats > 0:
+            self.viewer.add_splats("scene", *(x[:n_splats] for x in arrays))
+        for i, (start, stop) in enumerate(slices):
+            self.viewer.add_splats(f"drone:{i}", *(x[start:stop] for x in arrays))
+        self._n_drones = len(slices)
+
+    def update(self, sim: Sim, world: int = 0):
+        """Push the current drone poses of one world to the viewer.
+
+        Args:
+            sim: The simulation to visualize.
+            world: Index of the world whose drone poses are shown.
+        """
+        if self._n_drones == 0:
+            return
+        pos, quat = self._drone_mocap_poses(sim, world)
+        for i in range(self._n_drones):
+            self.viewer.update_pose(f"drone:{i}", pos[i], quat[i])
+
+    def close(self):
+        """Shut down the web server."""
+        self.viewer.close()
+
+    @staticmethod
+    @requires_mujoco_sync
+    def _drone_mocap_poses(sim: Sim, world: int) -> tuple[NDArray, NDArray]:
+        """Drone mocap positions and wxyz quaternions of one world, synced with the MuJoCo data."""
+        ids = sim.data.core.drone_mocap_ids
+        pos = np.asarray(sim.mjx_data.mocap_pos[world, ids])
+        quat = np.asarray(sim.mjx_data.mocap_quat[world, ids])  # MuJoCo quats are already wxyz
+        return pos, quat
diff --git a/crazyflow/utils.py b/crazyflow/utils.py
index 30b5d1b..5136444 100644
--- a/crazyflow/utils.py
+++ b/crazyflow/utils.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import inspect
+import os
 from collections.abc import Mapping
 from functools import partial
 from pathlib import Path
@@ -77,12 +78,17 @@ def to_device(data: Array, device: str) -> Array:
 
 
 def enable_cache(
-    cache_path: Path = Path("/tmp/jax_cache"),
+    cache_path: Path | None = None,
     min_entry_size_bytes: int = -1,
     min_compile_time_secs: int = 0,
     enable_xla_caches: bool = False,
 ):
-    """Enable JAX cache."""
+    """Enable JAX cache with the requested settings.
+
+    Cache path is user-dependent to avoid permission issues on multi-user machines.
+    """
+    if cache_path is None:
+        cache_path = Path(f"/tmp/jax_cache-{os.getuid()}")
     jax.config.update("jax_compilation_cache_dir", str(cache_path))
     jax.config.update("jax_persistent_cache_min_entry_size_bytes", min_entry_size_bytes)
     jax.config.update("jax_persistent_cache_min_compile_time_secs", min_compile_time_secs)
diff --git a/docs/api/index.md b/docs/api/index.md
index 4a502eb..6463c25 100644
--- a/docs/api/index.md
+++ b/docs/api/index.md
@@ -12,7 +12,8 @@ This section is auto-generated from the Crazyflow source code using [mkdocstring
 | `crazyflow.sim.functional` | Pure functional control API for use inside `jax.jit` |
 | `crazyflow.dynamics` | `Dynamics` enum and dynamics implementations |
 | `crazyflow.sim.integration` | `Integrator` enum, Euler, RK4, and symplectic Euler |
-| `crazyflow.sim.sensors` | Raycasting and sensor extraction utilities |
+| `crazyflow.sim.sensors` | Raycast depth (`sensors.depth`) and gaussian splat RGB (`sensors.splat`) sensors |
+| `crazyflow.sim.splat` | Gaussian splat plugin: `attach_splats` and the web-based `SplatViewer` |
 | `crazyflow.control` | `Control` enum |
 | `crazyflow.control.mellinger` | Mellinger controller data and parameters |
 | `crazyflow.envs` | Gymnasium vectorized environments |
diff --git a/docs/examples/index.md b/docs/examples/index.md
index ec3e825..76c876c 100644
--- a/docs/examples/index.md
+++ b/docs/examples/index.md
@@ -79,10 +79,10 @@ Inserting a random external force and torque into the step pipeline. The disturb
 
 ## Cameras and RGBD
 
-Offscreen rendering returns RGB and depth images on every frame. The FPV camera (`fpv_cam`) is attached to the drone and moves with it.
+Offscreen rendering returns RGB-D images on every frame. The FPV camera (`fpv_cam`) is attached to the drone and moves with it.
 
 <figure class="example-media">
-  <img src="../img/examples/cameras.gif" alt="RGB and depth camera outputs from a Crazyflow drone simulation">
+  <img src="../img/examples/cameras.gif" alt="RGB-D camera outputs from a Crazyflow drone simulation">
 </figure>
 
 <!-- notest: imported script, covered by tests/integration/test_examples.py -->
diff --git a/docs/gen_ref_pages.py b/docs/gen_ref_pages.py
index c42ec0a..180b630 100644
--- a/docs/gen_ref_pages.py
+++ b/docs/gen_ref_pages.py
@@ -49,7 +49,10 @@
     * [sim.data](crazyflow/sim/data.md)
     * [sim.functional](crazyflow/sim/functional.md)
     * [sim.integration](crazyflow/sim/integration.md)
-    * [sim.sensors](crazyflow/sim/sensors.md)
+    * [sim.sensors](crazyflow/sim/sensors/index.md)
+    * [sim.sensors.depth](crazyflow/sim/sensors/depth.md)
+    * [sim.sensors.splat](crazyflow/sim/sensors/splat.md)
+    * [sim.splat](crazyflow/sim/splat.md)
     * [sim.visualize](crazyflow/sim/visualize.md)
 * Dynamics
     * [dynamics](crazyflow/dynamics/index.md)
diff --git a/docs/get-started/quick-start.md b/docs/get-started/quick-start.md
index e578790..ffd1ac3 100644
--- a/docs/get-started/quick-start.md
+++ b/docs/get-started/quick-start.md
@@ -82,9 +82,9 @@ for _ in range(10):
     sim.state_control(cmd)
     sim.step(sim.freq // sim.control_freq)
 
-pos = sim.data.states.pos[0, 0]        # (3,)  — position in metres
-quat = sim.data.states.quat[0, 0]      # (4,)  — quaternion xyzw
-vel = sim.data.states.vel[0, 0]        # (3,)  — linear velocity m/s
+pos = sim.data.states.pos[0, 0]  # (3,)  — position in metres
+quat = sim.data.states.quat[0, 0]  # (4,)  — quaternion xyzw
+vel = sim.data.states.vel[0, 0]  # (3,)  — linear velocity m/s
 ang_vel = sim.data.states.ang_vel[0, 0]  # (3,)  — angular velocity rad/s
 ```
 
diff --git a/docs/user-guide/dynamics/batching.md b/docs/user-guide/dynamics/batching.md
index b3ab6f0..a97dbb3 100644
--- a/docs/user-guide/dynamics/batching.md
+++ b/docs/user-guide/dynamics/batching.md
@@ -71,9 +71,7 @@ nominal_J = dynamics.keywords["J"]
 
 @jax.jit
 def step(pos, quat, vel, ang_vel, cmd, rotor_vel, mass, J, J_inv):
-    return dynamics(
-        pos, quat, vel, ang_vel, cmd, rotor_vel=rotor_vel, mass=mass, J=J, J_inv=J_inv
-    )
+    return dynamics(pos, quat, vel, ang_vel, cmd, rotor_vel=rotor_vel, mass=mass, J=J, J_inv=J_inv)
 
 
 key, k1, k2 = jax.random.split(key, 3)
diff --git a/docs/user-guide/dynamics/dynamics-functions.md b/docs/user-guide/dynamics/dynamics-functions.md
index aba10e2..e5aed21 100644
--- a/docs/user-guide/dynamics/dynamics-functions.md
+++ b/docs/user-guide/dynamics/dynamics-functions.md
@@ -39,7 +39,10 @@ cmd = np.full((4,), 15_000.0)
 rotor_vel = np.full((4,), 12_000.0)
 
 pos_dot, quat_dot, vel_dot, ang_vel_dot, rotor_vel_dot = dynamics(
-    pos, quat, vel, ang_vel,
+    pos,
+    quat,
+    vel,
+    ang_vel,
     cmd,  # shape (4,) — motor RPMs
     rotor_vel,  # shape (4,) — current motor RPMs; pass None to skip rotor dynamics
 )
diff --git a/docs/user-guide/dynamics/index.md b/docs/user-guide/dynamics/index.md
index 60d86f9..1ee004b 100644
--- a/docs/user-guide/dynamics/index.md
+++ b/docs/user-guide/dynamics/index.md
@@ -28,10 +28,7 @@ from crazyflow.sim import Sim, Dynamics
 from crazyflow.control import Control
 
 # Force-torque and rotor_vel control modes require first_principles
-sim = Sim(
-    dynamics=Dynamics.first_principles,
-    control=Control.rotor_vel,
-)
+sim = Sim(dynamics=Dynamics.first_principles, control=Control.rotor_vel)
 sim.reset()
 ```
 
diff --git a/docs/user-guide/functional-api.md b/docs/user-guide/functional-api.md
index 9fc6fb0..002b28d 100644
--- a/docs/user-guide/functional-api.md
+++ b/docs/user-guide/functional-api.md
@@ -20,12 +20,14 @@ step, reset = sim.build_step_fn(), sim.build_reset_fn()
 cmd = jnp.zeros((1, 1, 4), dtype=jnp.float32)
 cmd = cmd.at[..., 3].set(float(data.params.mass[0, 0, 0]) * 9.81)
 
+
 @jax.jit
 def run(data, cmd):
     data = F.attitude_control(data, cmd)
     data = step(data, 10)
     return data
 
+
 data = run(data, cmd)
 assert data.states.pos.shape == (1, 1, 3)
 ```
@@ -92,6 +94,7 @@ step, reset = sim.build_step_fn(), sim.build_reset_fn()
 cmd = jnp.zeros((1, 1, 4), dtype=jnp.float32)
 cmd = cmd.at[..., 3].set(float(data.params.mass[0, 0, 0]) * 9.81)
 
+
 @jax.jit
 def simulate_episode(data, default_data, cmd):
     data = reset(data, default_data)
@@ -99,6 +102,7 @@ def simulate_episode(data, default_data, cmd):
     data = step(data, 10)
     return data
 
+
 data = simulate_episode(data, default_data, cmd)
 pos = data.states.pos[0, 0]
 ```
@@ -119,22 +123,18 @@ sim = Sim(control=Control.attitude, attitude_freq=50)
 sim.reset()
 
 # Place the drone 2 m above the floor, above the 1 m target
-data = sim.data.replace(
-    states=sim.data.states.replace(
-        pos=sim.data.states.pos.at[..., 2].set(2.0)
-    )
-)
+data = sim.data.replace(states=sim.data.states.replace(pos=sim.data.states.pos.at[..., 2].set(2.0)))
 step = sim.build_step_fn()
 
+
 def loss(cmd, data):
     data = data.replace(
-        controls=data.controls.replace(
-            attitude=data.controls.attitude.replace(staged_cmd=cmd)
-        )
+        controls=data.controls.replace(attitude=data.controls.attitude.replace(staged_cmd=cmd))
     )
     data = step(data, 10)
     return (data.states.pos[0, 0, 2] - 1.0) ** 2  # squared error to 1 m
 
+
 grad_fn = jax.jit(jax.grad(loss))
 
 cmd = jnp.zeros((1, 1, 4), dtype=jnp.float32)
diff --git a/docs/user-guide/gymnasium-envs.md b/docs/user-guide/gymnasium-envs.md
index 19b8cdb..39f2b25 100644
--- a/docs/user-guide/gymnasium-envs.md
+++ b/docs/user-guide/gymnasium-envs.md
@@ -70,6 +70,7 @@ from crazyflow.envs.drone_env import DroneEnv
 from crazyflow.sim.data import SimData
 from crazyflow.utils import leaf_replace
 
+
 def randomize(data: SimData, default_data: SimData, mask: jax.Array | None) -> SimData:
     key, subkey = jax.random.split(data.core.rng_key)
     data = data.replace(core=data.core.replace(rng_key=key))  # Make sure to update the rng_key
@@ -77,6 +78,7 @@ def randomize(data: SimData, default_data: SimData, mask: jax.Array | None) -> S
     states = leaf_replace(data.states, mask, pos=data.states.pos + noise)
     return data.replace(states=states)
 
+
 env = DroneEnv(num_envs=64, reset_randomization=randomize)
 env.close()
 ```
diff --git a/docs/user-guide/mujoco.md b/docs/user-guide/mujoco.md
index 5868437..5a5c1a2 100644
--- a/docs/user-guide/mujoco.md
+++ b/docs/user-guide/mujoco.md
@@ -149,7 +149,8 @@ from crazyflow.sim.data import SimData
 sim = Sim(n_worlds=1, n_drones=1)
 sim.reset()
 
-_mjx_data = sim.mjx_data   # captured in closure
+_mjx_data = sim.mjx_data  # captured in closure
+
 
 def check_contacts(sim_data: SimData, obstacle_mocap_pos: Array) -> Array:
     # Update obstacle positions and sync inside JIT
@@ -157,6 +158,7 @@ def check_contacts(sim_data: SimData, obstacle_mocap_pos: Array) -> Array:
     _, mjx_data = sync_sim2mjx(sim_data, mjx_data, sim.mjx_model)
     return mjx_data._impl.contact.dist < 0
 
+
 in_contact = check_contacts(sim.data, sim.mjx_data.mocap_pos)
 ```
 
diff --git a/docs/user-guide/oo-api.md b/docs/user-guide/oo-api.md
index d4e2af7..6cd2d91 100644
--- a/docs/user-guide/oo-api.md
+++ b/docs/user-guide/oo-api.md
@@ -21,9 +21,9 @@ sim = Sim(
     dynamics=Dynamics.first_principles,
     control=Control.state,
     integrator=Integrator.rk4,
-    freq=500,                       # dynamics update rate, Hz
-    state_freq=100,                 # state controller rate, Hz
-    attitude_freq=500,              # attitude controller rate, Hz
+    freq=500,  # dynamics update rate, Hz
+    state_freq=100,  # state controller rate, Hz
+    attitude_freq=500,  # attitude controller rate, Hz
     device="cpu",
 )
 sim.reset()
@@ -152,6 +152,7 @@ sim.step(50)
 
 # Reset only worlds 0 and 2, leaving 1 and 3 running
 import jax.numpy as jnp
+
 mask = jnp.array([True, False, True, False])
 sim.reset(mask=mask)
 ```
@@ -174,9 +175,9 @@ for _ in range(10):
     sim.step(sim.freq // sim.control_freq)
 
 # All drones in all worlds
-pos = sim.data.states.pos        # (2, 3, 3)
-quat = sim.data.states.quat      # (2, 3, 4)
-vel = sim.data.states.vel        # (2, 3, 3)
+pos = sim.data.states.pos  # (2, 3, 3)
+quat = sim.data.states.quat  # (2, 3, 4)
+vel = sim.data.states.vel  # (2, 3, 3)
 
 # Drone 1 in world 0
 pos_w0_d1 = sim.data.states.pos[0, 1]  # (3,)
@@ -208,12 +209,14 @@ from crazyflow.sim.data import SimData
 from crazyflow.sim.pipeline import append_fn
 from crazyflow.utils import leaf_replace
 
+
 def randomize_mass(data: SimData, default_data: SimData, mask: Array | None = None) -> SimData:
     key, mass_key = jax.random.split(data.core.rng_key)
     mass = data.params.mass + jax.random.normal(mass_key, data.params.mass.shape) * 2e-3
     params = leaf_replace(data.params, mask, mass=mass)
     return data.replace(params=params, core=data.core.replace(rng_key=key))
 
+
 sim = Sim(n_worlds=4, n_drones=1)
 append_fn(sim.reset_pipeline, randomize_mass)
 sim.build_reset_fn()
diff --git a/docs/user-guide/pipelines.md b/docs/user-guide/pipelines.md
index 1e07fbd..aeba866 100644
--- a/docs/user-guide/pipelines.md
+++ b/docs/user-guide/pipelines.md
@@ -73,14 +73,15 @@ from crazyflow.sim import Sim
 from crazyflow.sim.data import SimData
 from crazyflow.sim.pipeline import append_fn
 
+
 def randomize_initial_pos(data: SimData, default_data: SimData, mask: Array | None) -> SimData:
     key, subkey = jax.random.split(data.core.rng_key)
     noise = jax.random.normal(subkey, data.states.pos.shape) * 0.1  # ±10 cm
     return data.replace(
-        states=data.states.replace(pos=data.states.pos + noise),
-        core=data.core.replace(rng_key=key),
+        states=data.states.replace(pos=data.states.pos + noise), core=data.core.replace(rng_key=key)
     )
 
+
 sim = Sim(n_worlds=16)
 append_fn(sim.reset_pipeline, randomize_initial_pos)
 sim.build_reset_fn()  # recompile
@@ -133,10 +134,12 @@ from crazyflow.sim import Sim
 from crazyflow.sim.data import SimData
 from crazyflow.sim.pipeline import append_fn
 
+
 def my_step_stage(data: SimData) -> SimData:
     # JAX operations only — return updated data
     return data.replace(states=data.states.replace(pos=data.states.pos + 0.01))
 
+
 sim = Sim()
 append_fn(sim.step_pipeline, my_step_stage)
 sim.build_step_fn()
diff --git a/docs/user-guide/sim-overview.md b/docs/user-guide/sim-overview.md
index e36af8e..67f46c1 100644
--- a/docs/user-guide/sim-overview.md
+++ b/docs/user-guide/sim-overview.md
@@ -57,6 +57,7 @@ sim = Sim(n_worlds=1, n_drones=1)
 sim.reset()
 
 import jax.numpy as jnp
+
 new_pos = sim.data.states.pos.at[..., 2].add(1.0)
 sim.data = sim.data.replace(states=sim.data.states.replace(pos=new_pos))
 ```
diff --git a/docs/user-guide/splats.md b/docs/user-guide/splats.md
new file mode 100644
index 0000000..435f52a
--- /dev/null
+++ b/docs/user-guide/splats.md
@@ -0,0 +1,105 @@
+# Gaussian Splat Rendering
+
+Crazyflow renders photorealistic images with 3D gaussian splatting through [splax](https://github.com/learnsyslab/splax). A web-based viewer visualizes the simulation as splats. A camera sensor renders batched RGB(-D) images across all worlds. Both combine a static scene splat with one splat per drone that follows the drone's pose.
+
+<!-- TODO: Add image of the web viewer showing the hall splat with a drone splat mid-flight -->
+
+## Installation
+
+Install crazyflow with the `splats` extra. It pulls in splax with its viewer dependencies.
+
+```bash
+pip install "crazyflow[splats]"
+```
+
+The web viewer runs on any device. The camera sensor rasterizes with CUDA kernels and requires an NVIDIA GPU.
+
+## Demo assets
+
+Demo splats of a flight hall and a Crazyflie drone are published in the [amacati/splats](https://huggingface.co/datasets/amacati/splats) dataset. splax's `fetch` downloads a URL into a local cache on first use and returns the cached path afterwards. The cache lives at `~/.cache/splax` and can be moved with the `SPLAX_CACHE` environment variable. Passing `force=True` refreshes the cache.
+
+<!-- notest: requires splax and network access -->
+```{ .python notest }
+from splax.io import fetch
+
+assets = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+scene = fetch(f"{assets}/robot_hall.ply")
+drone = fetch(f"{assets}/cf21B_500.ply")
+```
+
+## Attaching splats
+
+Splats are loaded from 3D gaussian splatting `.ply` files and stored in the simulation's plugin data (`sim.data.plugins`). They therefore persist across resets and travel with the sim data through `jax.jit`. The files must be aligned with the simulation frames. The scene splat is aligned with the MuJoCo world frame, with +z up and metric scale. The drone splat is aligned with the drone body frame and centered on its center of mass.
+
+<!-- notest: requires splax and splat .ply files -->
+```{ .python notest }
+from crazyflow.sim import Sim
+from crazyflow.sim.splat import attach_splats
+
+sim = Sim(n_worlds=4, n_drones=2)
+attach_splats(sim, scene=scene, drone=drone)
+```
+
+The drone splat is replicated once per drone. Both arguments are optional. Pass only `scene` for a static environment, or only `drone` for splats in an empty world.
+
+## Web viewer
+
+`SplatViewer` renders the simulation in a web-based viewer served at `http://localhost:8080`. Constructing the viewer streams the splats to the browser once. `update` then only pushes the current drone poses, so the viewer runs at real-time rates on any device. The viewer is independent of `sim.render()`. Both can be open at the same time, e.g. to compare the mesh scene and the splat scene side by side.
+
+<!-- notest: requires splax and splat .ply files -->
+```{ .python notest }
+from crazyflow.sim.splat import SplatViewer
+
+viewer = SplatViewer(sim)
+for _ in range(500):
+    sim.step(sim.freq // sim.control_freq)
+    viewer.update(sim, world=0)
+viewer.close()
+```
+
+See `examples/rendering/splat_viewer.py` for a complete viewer demo.
+
+## Camera sensor
+
+`render_splat_rgb` from `crazyflow.sim.sensors.splat` renders RGB images from any model camera, batched over all worlds and drones. It uses splax's CUDA rasterizer, so the simulation must run on the GPU. Scene gaussians stay static. Each drone's gaussians follow its current pose via splax's dynamic transforms, without copying the splat buffer.
+
+<!-- notest: requires splax and a CUDA GPU -->
+```{ .python notest }
+from crazyflow.sim.sensors.splat import build_render_splat_fn, render_splat_rgb
+
+sim = Sim(n_worlds=4, n_drones=2, device="gpu")
+attach_splats(sim, scene=scene, drone=drone)
+imgs = render_splat_rgb(sim, resolution=(320, 240))  # (4, 2, 240, 320, 3) in [0, 1]
+imgs = render_splat_rgb(sim, drones=0, resolution=(320, 240))  # single drone: (4, 1, 240, 320, 3)
+
+# Bake input args into a compiled function. The result is a pure function of sim.data, so it can be
+# traced, jitted, and differentiated.
+render_fn = build_render_splat_fn(sim, resolution=(320, 240))
+imgs = render_fn(sim.data)
+```
+
+`exclude_self=True` hides each drone's own splat from its own camera, so a drone sees the others but not itself.
+
+See `examples/rendering/splat_camera.py` for a matplotlib-based camera sensor demo.
+
+## Depth sensor
+
+`render_splat_rgbd` adds depth as a fourth channel.
+
+<!-- notest: requires splax and a CUDA GPU -->
+```{ .python notest }
+from crazyflow.sim.sensors.splat import build_render_splat_rgbd_fn, render_splat_rgbd
+
+rgbd = render_splat_rgbd(sim, resolution=(320, 240), max_range=8.0)  # (4, 2, 240, 320, 4)
+rgb, depth = rgbd[..., :3], rgbd[..., 3]
+
+# Bake camera intrinsics, sensor range, and splat metadata into a compiled function
+render_fn = build_render_splat_rgbd_fn(sim, resolution=(320, 240), max_range=8.0)
+rgbd = render_fn(sim.data)
+```
+
+Gaussians are semi-transparent, so a pixel only carries a usable depth once enough of them accumulate behind it. Pixels whose coverage stays below `alpha_threshold` count as empty space and report `max_range`, which is also the value depth is clipped to.
+
+The values are depth along the camera's optical axis in meters. `render_depth` from `crazyflow.sim.sensors.depth` raycasts the MuJoCo geometry instead and returns ray distances, so the two are not interchangeable.
+
+See `examples/rendering/splat_depth.py` for a depth camera flying a lap around the hall.
diff --git a/examples/rendering/raycasting.py b/examples/rendering/raycasting.py
index 42bf00a..6be66c5 100644
--- a/examples/rendering/raycasting.py
+++ b/examples/rendering/raycasting.py
@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 
 from crazyflow.sim import Sim
-from crazyflow.sim.sensors import build_render_depth_fn, render_depth
+from crazyflow.sim.sensors.depth import build_render_depth_fn, render_depth
 
 
 def main(plot: bool = False):
diff --git a/examples/rendering/splat_camera.py b/examples/rendering/splat_camera.py
new file mode 100644
index 0000000..d24e7ed
--- /dev/null
+++ b/examples/rendering/splat_camera.py
@@ -0,0 +1,92 @@
+"""Animate gaussian splat rendering from two drones looking at each other.
+
+Requires splax and a CUDA-capable GPU because the splat camera sensor uses splax's GPU rasterizer.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+os.environ["SCIPY_ARRAY_API"] = "1"
+os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+from splax.io import fetch
+
+from crazyflow.sim import Sim
+from crazyflow.sim.sensors.splat import render_splat_rgb
+from crazyflow.sim.splat import attach_splats
+
+ASSETS_URL = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+
+RADIUS = 0.5
+HEIGHT = 1.0
+
+
+def control(t: float, n_worlds: int, n_drones: int) -> np.ndarray:
+    """Circle both drones around the center, each yawed to look across at its partner."""
+    drones = np.arange(n_drones)[None, :]
+    angle = t + (2 * np.pi / n_drones) * drones
+    cmd = np.zeros((n_worlds, n_drones, 13))
+    cmd[..., 0] = RADIUS * np.cos(angle)
+    cmd[..., 1] = RADIUS * np.sin(angle)
+    cmd[..., 2] = HEIGHT
+    cmd[..., 9] = angle + np.pi  # yaw faces the opposite point on the circle, where the partner is
+    return cmd
+
+
+def main(show_plot: bool = False):
+    """Render four splat camera views into a matplotlib window."""
+    sim = Sim(n_worlds=2, n_drones=2, control="state", device="gpu")
+    scene = fetch(f"{ASSETS_URL}/robot_hall.ply")
+    drone = fetch(f"{ASSETS_URL}/{sim.drone}.ply")
+    attach_splats(sim, scene=scene, drone=drone)
+
+    angle = (2 * np.pi / sim.n_drones) * np.arange(sim.n_drones)
+    x, y, z = RADIUS * np.cos(angle), RADIUS * np.sin(angle), np.full_like(angle, HEIGHT)
+    pos = np.stack([x, y, z], axis=-1)
+    quat = R.from_euler("z", (angle + np.pi)[:, None]).as_quat()
+    states = sim.data.states.replace(pos=sim.data.states.pos.at[...].set(pos))
+    states = states.replace(quat=sim.data.states.quat.at[...].set(quat))
+    sim.data = sim.data.replace(states=states)
+
+    duration = 6
+    fps = 30
+    n_frames = int(duration * fps)
+    resolution = (320, 240)
+
+    fig, axes = plt.subplots(sim.n_worlds, sim.n_drones, figsize=(10, 7))
+    img = np.zeros((resolution[1], resolution[0], 3), dtype=np.float32)
+    ims = np.empty((sim.n_worlds, sim.n_drones), dtype=object)
+    for w in range(sim.n_worlds):
+        for d in range(sim.n_drones):
+            ax = axes[w, d]
+            ims[w, d] = ax.imshow(img)
+            ax.set_title(f"World {w}, drone {d} FPV")
+            ax.axis("off")
+    fig.tight_layout()
+
+    if show_plot:
+        plt.show(block=False)
+        for _ in range(n_frames):
+            t = sim.data.core.steps[0, 0] / sim.freq
+            sim.state_control(control(t, sim.n_worlds, sim.n_drones))
+            sim.step(sim.freq // fps)
+            # Render each drone's fpv across all worlds, hiding each drone from its own view.
+            views = np.asarray(render_splat_rgb(sim, resolution=resolution))
+            for w in range(sim.n_worlds):
+                for d in range(sim.n_drones):
+                    ims[w, d].set_data(views[w, d])
+            fig.canvas.draw_idle()
+            plt.pause(1e-12)
+        plt.close(fig)
+    sim.close()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("jax").setLevel(logging.WARNING)
+    main(show_plot=True)
diff --git a/examples/rendering/splat_depth.py b/examples/rendering/splat_depth.py
new file mode 100644
index 0000000..ba15232
--- /dev/null
+++ b/examples/rendering/splat_depth.py
@@ -0,0 +1,88 @@
+"""Animate the splat depth camera of a drone flying an elliptical lap through the flight hall.
+
+Requires splax and a CUDA-capable GPU because the splat camera sensor uses splax's GPU rasterizer.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+os.environ["SCIPY_ARRAY_API"] = "1"
+os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+from splax.io import fetch
+
+from crazyflow.sim import Sim
+from crazyflow.sim.sensors.splat import build_render_splat_rgbd_fn
+from crazyflow.sim.splat import attach_splats
+
+ASSETS_URL = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+
+# Elliptical lap, inset from the hall walls so the camera keeps them in view and clears them
+CENTER = np.array([0.0, 0.0])
+RADII = np.array([9.0, 3.5])
+HEIGHT = 1.5
+MAX_RANGE = 12.0
+RESOLUTION = (320, 240)
+DURATION = 40
+FPS = 30
+
+
+def control(t: float) -> np.ndarray:
+    """State setpoint a fraction ``t`` into one lap of the ellipse.
+
+    Args:
+        t: Progress along the lap, wrapped into [0, 1).
+
+    Returns:
+        A state command placing the drone on the ellipse with its yaw along the tangent.
+    """
+    angle = 2 * np.pi * t
+    cmd = np.zeros((1, 1, 13))
+    cmd[..., :2] = CENTER + RADII * np.array([np.cos(angle), np.sin(angle)])
+    cmd[..., 2] = HEIGHT
+    cmd[..., 9] = np.arctan2(RADII[1] * np.cos(angle), -RADII[0] * np.sin(angle))
+    return cmd
+
+
+def main(show_plot: bool = False):
+    """Fly the lap and animate the depth channel of the drone's fpv camera."""
+    sim = Sim(control="state", device="gpu")
+    scene = fetch(f"{ASSETS_URL}/robot_hall.ply")
+    drone = fetch(f"{ASSETS_URL}/{sim.drone}.ply")
+    attach_splats(sim, scene=scene, drone=drone)
+    render = build_render_splat_rgbd_fn(sim, drones=0, resolution=RESOLUTION, max_range=MAX_RANGE)
+
+    # Start on the lap so the controller does not have to fly in from the origin first
+    cmd = control(0.0)
+    states = sim.data.states.replace(pos=sim.data.states.pos.at[..., :].set(cmd[..., :3]))
+    states = states.replace(quat=states.quat.at[...].set(R.from_euler("z", cmd[..., 9]).as_quat()))
+    sim.data = sim.data.replace(states=states)
+
+    fig, ax = plt.subplots(figsize=(7, 5))
+    im = ax.imshow(np.zeros(RESOLUTION[::-1]), cmap="turbo_r", vmin=0.0, vmax=MAX_RANGE)
+    ax.set_title("Splat depth camera, fpv")
+    ax.axis("off")
+    fig.colorbar(im, ax=ax, label="depth along the optical axis (m)")
+    fig.tight_layout()
+
+    if show_plot:
+        plt.show(block=False)
+        for i in range(DURATION * FPS):
+            sim.state_control(control(i / (DURATION * FPS)))
+            sim.step(sim.freq // FPS)
+            im.set_data(np.asarray(render(sim.data))[0, 0, ..., 3])
+            fig.canvas.draw_idle()
+            fig.canvas.flush_events()
+        plt.close(fig)
+    sim.close()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("jax").setLevel(logging.WARNING)
+    main(show_plot=True)
diff --git a/examples/rendering/splat_gradients.py b/examples/rendering/splat_gradients.py
new file mode 100644
index 0000000..4c516c4
--- /dev/null
+++ b/examples/rendering/splat_gradients.py
@@ -0,0 +1,176 @@
+"""Compare splat-rendered FPV gradients with known z-position and yaw gradients.
+
+A reference image is rendered once, then its photometric error is differentiated over independent
+z-position and yaw sweeps. Requires splax and a CUDA-capable GPU.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from functools import partial
+from typing import TYPE_CHECKING
+
+os.environ["SCIPY_ARRAY_API"] = "1"
+os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+
+import jax
+import jax.numpy as jnp
+import matplotlib.pyplot as plt
+import mujoco
+import numpy as np
+import splax
+from scipy.spatial.transform import Rotation as R
+from splax.io import fetch
+
+from crazyflow.sim import Sim
+from crazyflow.sim.sensors.splat import camera_intrinsics
+from crazyflow.sim.splat import SPLAT_KEYS, attach_splats
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from jax import Array
+    from numpy.typing import NDArray
+
+ASSETS_URL = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+
+BASELINE_POSITION = np.array([5.0, 0.0, 2.0], dtype=np.float32)
+BASELINE_QUATERNION = np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32)  # xyzw
+
+RESOLUTION = (360, 240)
+# Limit to 100 to prevent OOM during testing when JAX has already claimed significant GPU memory
+N_SAMPLES = 100
+MAX_Z_OFFSET = 0.025
+MAX_YAW_DEGREES = 0.1
+
+
+def _camera_viewmats(
+    z_offsets: Array, yaw_angles: Array, camera_position: Array, camera_rotation: R
+) -> Array:
+    """Create splax world-to-camera matrices for z/yaw drone poses."""
+    n_views = z_offsets.shape[0]
+    position = jnp.broadcast_to(jnp.asarray(BASELINE_POSITION), (n_views, 3))
+    position = position.at[:, 2].add(z_offsets)
+
+    body_rotation = R.from_quat(jnp.asarray(BASELINE_QUATERNION)) * R.from_euler(
+        "z", yaw_angles[:, None]
+    )
+    camera_world_position = position + body_rotation.apply(camera_position)
+
+    # MuJoCo cameras use OpenGL axes. Rotate them 180 degrees around x to obtain the OpenCV axes
+    # expected by splax, then invert the complete camera-to-world rotation.
+    camera_to_world = body_rotation * camera_rotation * R.from_euler("x", jnp.pi)
+    world_to_camera_rotation = camera_to_world.inv().as_matrix()
+    world_to_camera_translation = -(world_to_camera_rotation @ camera_world_position[..., None])[
+        ..., 0
+    ]
+
+    viewmats = jnp.broadcast_to(jnp.eye(4, dtype=z_offsets.dtype), (n_views, 4, 4))
+    viewmats = viewmats.at[:, :3, :3].set(world_to_camera_rotation)
+    return viewmats.at[:, :3, 3].set(world_to_camera_translation)
+
+
+def _build_renderer(sim: Sim) -> Callable[[Array, Array], Array]:
+    """Build a differentiable FPV renderer parameterized by z and yaw."""
+    camera_id = mujoco.mj_name2id(sim.mj_model, mujoco.mjtObj.mjOBJ_CAMERA, "fpv_cam:0")
+    if camera_id < 0:
+        raise ValueError("Camera 'fpv_cam:0' not found in the model")
+
+    camera_position = jnp.asarray(sim.mj_model.cam_pos[camera_id], dtype=jnp.float32)
+    camera_quaternion = jnp.roll(
+        jnp.asarray(sim.mj_model.cam_quat[camera_id], dtype=jnp.float32), -1
+    )  # wxyz -> xyzw
+    camera_rotation = R.from_quat(camera_quaternion)
+
+    f, c = camera_intrinsics(sim.mj_model, camera_id, RESOLUTION)
+    arrays = tuple(sim.data.plugins[key] for key in SPLAT_KEYS)
+    background = jnp.zeros(3, dtype=arrays[0].dtype)
+    image_shape = (RESOLUTION[1], RESOLUTION[0])
+    render = partial(splax.render, *arrays, background=background, img_shape=image_shape, f=f, c=c)
+
+    def render_views(z_offsets: Array, yaw_angles: Array) -> Array:
+        viewmats = _camera_viewmats(z_offsets, yaw_angles, camera_position, camera_rotation)
+        return jax.vmap(lambda viewmat: render(viewmat=viewmat)[0])(viewmats)
+
+    return render_views
+
+
+def _plot_results(
+    experiments: tuple[tuple[NDArray, NDArray, NDArray, str, str], ...], show_plot: bool
+) -> None:
+    """Plot normalized gradient comparisons for both pose sweeps."""
+    fig, axes = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True)
+    for axis, (x_values, image_gradient, pose_gradient, title, x_label) in zip(
+        axes, experiments, strict=True
+    ):
+        image_gradient = image_gradient / np.max(np.abs(image_gradient))
+        pose_gradient = pose_gradient / np.max(np.abs(pose_gradient))
+
+        axis.plot(x_values, image_gradient, label="FPV photometric gradient", linewidth=2)
+        axis.plot(x_values, pose_gradient, "--", label="Known pose gradient", linewidth=2)
+        axis.axvline(0.0, color="black", linewidth=1, alpha=0.4)
+        axis.set_title(title)
+        axis.set_xlabel(x_label)
+        axis.set_ylabel("Normalized gradient")
+        axis.set_ylim(-1.1, 1.1)
+        axis.grid(alpha=0.25)
+        axis.legend()
+
+    fig.suptitle(
+        "Differentiable FPV pose gradients\n"
+        "Each gradient curve is normalized by its own maximum absolute value"
+    )
+    if show_plot:
+        plt.show()
+    plt.close(fig)
+
+
+def main(show_plot: bool = False) -> None:
+    """Compare photometric and known pose gradients for independent z and yaw sweeps."""
+    sim = Sim(n_worlds=1, n_drones=1, device="gpu")
+    scene = fetch(f"{ASSETS_URL}/robot_hall.ply")
+    attach_splats(sim, scene=scene)
+    render = _build_renderer(sim)
+
+    z_offsets = jnp.linspace(-MAX_Z_OFFSET, MAX_Z_OFFSET, N_SAMPLES, dtype=jnp.float32)
+    yaw_angles = jnp.deg2rad(
+        jnp.linspace(-MAX_YAW_DEGREES, MAX_YAW_DEGREES, N_SAMPLES, dtype=jnp.float32)
+    )
+    zeros = jnp.zeros_like(z_offsets)
+    reference_image = jax.lax.stop_gradient(render(zeros[:1], zeros[:1])[0])
+
+    def photometric_objective(z_offsets: Array, yaw_angles: Array) -> Array:
+        images = render(z_offsets, yaw_angles)
+        return jnp.mean((images - reference_image) ** 2, axis=(1, 2, 3)).sum()
+
+    pose_gradient = jax.jit(jax.grad(photometric_objective, argnums=(0, 1)))
+    z_gradient, _ = pose_gradient(z_offsets, zeros)
+    _, yaw_gradient = pose_gradient(zeros, yaw_angles)
+    sim.close()
+
+    z_offsets = np.asarray(z_offsets)
+    yaw_angles = np.asarray(yaw_angles)
+    experiments = (
+        (
+            100.0 * z_offsets,
+            np.asarray(z_gradient),
+            z_offsets,
+            "Z translation only",
+            "z offset from baseline (cm)",
+        ),
+        (
+            np.rad2deg(yaw_angles),
+            np.asarray(yaw_gradient),
+            yaw_angles,
+            "Yaw rotation only",
+            "yaw offset from baseline (degrees)",
+        ),
+    )
+    _plot_results(experiments, show_plot)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("jax").setLevel(logging.WARNING)
+    main(show_plot=True)
diff --git a/examples/rendering/splat_viewer.py b/examples/rendering/splat_viewer.py
new file mode 100644
index 0000000..a761c4d
--- /dev/null
+++ b/examples/rendering/splat_viewer.py
@@ -0,0 +1,54 @@
+"""Render the simulation as gaussian splats in a web-based viewer.
+
+Requires splax (`pip install crazyflow[splats]`) and gaussian splat .ply files aligned to the
+simulation frames. The demo splats are downloaded from the amacati/splats dataset on first use and
+cached locally.
+
+The viewer streams the splats to the browser once and then only updates the drone poses, so it
+runs in real time on any device. The splat camera sensor renders with CUDA kernels and therefore
+requires a GPU.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+
+import numpy as np
+from splax.io import fetch
+
+from crazyflow.sim import Sim
+from crazyflow.sim.splat import SplatViewer, attach_splats
+
+logger = logging.getLogger(__name__)
+ASSETS_URL = "https://huggingface.co/datasets/amacati/splats/resolve/main"
+
+
+def control(t: float) -> np.ndarray:
+    cmd = np.zeros((1, 1, 13))
+    cmd[..., :3] = [0.5 * (np.cos(t) - 1), 0.5 * np.sin(t), 1.0 + 0.2 * np.sin(0.5 * t)]
+    return cmd
+
+
+def main():
+    sim = Sim(control="state")
+    scene = fetch(f"{ASSETS_URL}/robot_hall.ply")
+    drone = fetch(f"{ASSETS_URL}/{sim.drone}.ply")
+    attach_splats(sim, scene=scene, drone=drone)
+    viewer = SplatViewer(sim)
+
+    for i in range(2000):
+        t_start = time.perf_counter()
+        sim.state_control(control(i / sim.control_freq))
+        sim.step(sim.freq // sim.control_freq)
+        viewer.update(sim)
+        time.sleep(max(0.0, 1 / sim.control_freq - (time.perf_counter() - t_start)))
+
+    viewer.close()
+    sim.close()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    logging.getLogger("jax").setLevel(logging.WARNING)
+    main()
diff --git a/pixi.lock b/pixi.lock
index 3449b24..cbe91b9 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,7 +1,16 @@
 version: 7
 platforms:
 - name: linux-64
+  virtual-packages:
+  - __unix=0=0
+  - __linux=4.18
+  - __glibc=2.28
+  - __archspec=0=x86_64
 - name: osx-arm64
+  virtual-packages:
+  - __unix=0=0
+  - __osx=13.0
+  - __archspec=0=m1
 environments:
   benchmark:
     channels:
@@ -14,13 +23,18 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py312h0a2e395_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.2-h27c8c51_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.63.0-py312h8a5da7c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.15-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h54a6638_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.5.0-py312h0a2e395_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.2.0-hdb68285_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-8_h4a7cf45_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_1.conda
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   - python-dateutil >=2.7
   - python_abi 3.12.* *_cp312
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-  - python_abi 3.13.* *_cp313
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+  - python_abi 3.13.* *_cp313
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+  - lcms2 >=2.19.1,<3.0a0
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-  - python_abi 3.13.* *_cp313
-  - zlib-ng >=2.3.3,<2.4.0a0
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.11.1-py313hcd51b16_1.conda
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.14-h6add32d_100_cp313.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.15.17-h6a952e8_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.16.1-h462bb3b_0.conda
   noarch: python
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+  - pkg:pypi/tornado?source=hash-mapping
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+  - linkify-it-py ; extra == 'doc'
+  - tabulate ; extra == 'doc'
+  - click<8.3.0 ; extra == 'dev'
+  - spin ; extra == 'dev'
+  - mypy==1.19.1 ; extra == 'dev'
+  - pyrefly==0.63.0 ; extra == 'dev'
+  - typing-extensions ; extra == 'dev'
+  - types-psutil ; extra == 'dev'
+  - pycodestyle ; extra == 'dev'
+  - ruff>=0.12.0 ; extra == 'dev'
+  - cython-lint>=0.12.2 ; extra == 'dev'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/de/e4/1ba6f44e491c4eece978685230dde56b14d51a0365bc1b774ddaa94d14cd/pyopengl-3.1.10-py3-none-any.whl
   name: pyopengl
   version: 3.1.10
@@ -6829,6 +8017,21 @@ packages:
   version: 7.35.1
   sha256: 74758715c53d7158fb76caf4f0cfdacc5329a4b1bb994f865d6cf302d413a1c4
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/e4/d3/5268aeabf2ad82658c4e2ff3a060648d0f02f3926cb53247c0e4d0dab49e/griffelib-2.1.0-py3-none-any.whl
+  name: griffelib
+  version: 2.1.0
+  sha256: cc7b3d2d2865ad0b909fcc38086e3f554b5ea7acbaa7bbb7ecaa3f5dfb7d9f00
+  requires_dist:
+  - pip>=24.0 ; extra == 'pypi'
+  - platformdirs>=4.2 ; extra == 'pypi'
+  - wheel>=0.42 ; extra == 'pypi'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/e5/2e/22216db429690aa51fa87d2e2b5e6b610c5a37b9df7768da2927f52f8704/glfw-2.10.2-py2.py3-none-macosx_11_0_arm64.whl
+  name: glfw
+  version: 2.10.2
+  sha256: c000b8a5e5fb4374b2c18d12347255ccb92001b9bcf80ef74c56072acbb98fe3
+  requires_dist:
+  - glfw-preview ; extra == 'preview'
 - pypi: https://files.pythonhosted.org/packages/e5/4c/93d0f85318da65923e4b91c1c2ff03d8a458cbefebe3bc612a6693c7906d/fire-0.7.1-py3-none-any.whl
   name: fire
   version: 0.7.1
@@ -6928,50 +8131,15 @@ packages:
   - pylint>=2.6.0 ; extra == 'dev'
   - pyink ; extra == 'dev'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/ec/ae/db19f8ab842e9b724bf5dbb7db29302a91f1e55bc4d04b1025d6d605a2c5/scipy-1.17.1-cp313-cp313-macosx_12_0_arm64.whl
-  name: scipy
-  version: 1.17.1
-  sha256: 6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f
+- pypi: https://files.pythonhosted.org/packages/eb/a3/b69efbf4143b5b9859b977770bbbabcc2796b702fa69dc40271e45cd5a56/prometheus_client-0.26.0-py3-none-any.whl
+  name: prometheus-client
+  version: 0.26.0
+  sha256: fa93d06737aa02bacd05794768508bb97d2fbee28cb3bca04eaae92f0ca953d6
   requires_dist:
-  - numpy>=1.26.4,<2.7
-  - pytest>=8.0.0 ; extra == 'test'
-  - pytest-cov ; extra == 'test'
-  - pytest-timeout ; extra == 'test'
-  - pytest-xdist ; extra == 'test'
-  - asv ; extra == 'test'
-  - mpmath ; extra == 'test'
-  - gmpy2 ; extra == 'test'
-  - threadpoolctl ; extra == 'test'
-  - scikit-umfpack ; extra == 'test'
-  - pooch ; extra == 'test'
-  - hypothesis>=6.30 ; extra == 'test'
-  - array-api-strict>=2.3.1 ; extra == 'test'
-  - cython ; extra == 'test'
-  - meson ; extra == 'test'
-  - ninja ; sys_platform != 'emscripten' and extra == 'test'
-  - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
-  - intersphinx-registry ; extra == 'doc'
-  - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
-  - sphinx-copybutton ; extra == 'doc'
-  - sphinx-design>=0.4.0 ; extra == 'doc'
-  - matplotlib>=3.5 ; extra == 'doc'
-  - numpydoc ; extra == 'doc'
-  - jupytext ; extra == 'doc'
-  - myst-nb>=1.2.0 ; extra == 'doc'
-  - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
-  - jupyterlite-pyodide-kernel ; extra == 'doc'
-  - linkify-it-py ; extra == 'doc'
-  - tabulate ; extra == 'doc'
-  - click<8.3.0 ; extra == 'dev'
-  - spin ; extra == 'dev'
-  - mypy==1.10.0 ; extra == 'dev'
-  - typing-extensions ; extra == 'dev'
-  - types-psutil ; extra == 'dev'
-  - pycodestyle ; extra == 'dev'
-  - ruff>=0.12.0 ; extra == 'dev'
-  - cython-lint>=0.12.2 ; extra == 'dev'
-  requires_python: '>=3.11'
+  - twisted ; extra == 'twisted'
+  - aiohttp ; extra == 'aiohttp'
+  - django ; extra == 'django'
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/ee/1b/3075eb67fe66e19db059f0a25744c4e56978a309603a20e1d3353d545b5e/mkdocs_gen_files-0.6.1-py3-none-any.whl
   name: mkdocs-gen-files
   version: 0.6.1
@@ -7001,12 +8169,17 @@ packages:
   - pytest-enabler>=3.4 ; extra == 'enabler'
   - pytest-mypy>=1.0.1 ; platform_python_implementation != 'PyPy' and extra == 'type'
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/f5/5f/f17563f28ff03c7b6799c50d01d5d856a1d55f2676f537ca8d28c7f627cd/scipy-1.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/f3/ab/a7653bce9a3b204be6a6931767a9e23595807bb84790ce6685e4d7e5bd08/nh3-0.3.6-cp38-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl
+  name: nh3
+  version: 0.3.6
+  sha256: a43ebd7543555c3ac1bc353023d0794e75cb76f6f18f19c32e95441496c0cc25
+  requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/f6/af/e8fe5fb136f51e2b01678b92cb4106d10d8cd68ec147ead2e7cb0ac75398/scipy-1.18.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
   name: scipy
-  version: 1.17.1
-  sha256: 581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464
+  version: 1.18.0
+  sha256: a46f9273dbd0eb1cefba61c9b8648b4dfe3cbc14a080176f9a73e44b8336dc7f
   requires_dist:
-  - numpy>=1.26.4,<2.7
+  - numpy>=2.0.0,<2.8
   - pytest>=8.0.0 ; extra == 'test'
   - pytest-cov ; extra == 'test'
   - pytest-timeout ; extra == 'test'
@@ -7022,6 +8195,7 @@ packages:
   - cython ; extra == 'test'
   - meson ; extra == 'test'
   - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - scipy-doctest>=2.0.0 ; extra == 'test'
   - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
   - intersphinx-registry ; extra == 'doc'
   - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
@@ -7038,21 +8212,144 @@ packages:
   - tabulate ; extra == 'doc'
   - click<8.3.0 ; extra == 'dev'
   - spin ; extra == 'dev'
-  - mypy==1.10.0 ; extra == 'dev'
+  - mypy==1.19.1 ; extra == 'dev'
+  - pyrefly==0.63.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
   - types-psutil ; extra == 'dev'
   - pycodestyle ; extra == 'dev'
   - ruff>=0.12.0 ; extra == 'dev'
   - cython-lint>=0.12.2 ; extra == 'dev'
-  requires_python: '>=3.11'
-- pypi: https://files.pythonhosted.org/packages/f7/a1/47c08a81760cae84c4a4aa720f3fc1ce3bac6f7aafa5ab82c302d7946f07/jax_cuda12_pjrt-0.10.1-py3-none-manylinux_2_27_x86_64.whl
+  requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/f9/1c/01bfd571a64e7f270e6bab5e33777debe0edc56759233ce84f27dec92d14/tqdm-4.70.0-py3-none-any.whl
+  name: tqdm
+  version: 4.70.0
+  sha256: 7f585706bfddbdebf89daac705b2dfcc16890130727d3197ca62c732b4310953
+  requires_dist:
+  - colorama ; sys_platform == 'win32'
+  - requests ; extra == 'discord'
+  - envwrap ; extra == 'discord'
+  - slack-sdk ; extra == 'slack'
+  - envwrap ; extra == 'slack'
+  - requests ; extra == 'telegram'
+  - envwrap ; extra == 'telegram'
+  - ipywidgets>=6 ; extra == 'notebook'
+  requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/fd/3c/6a2bf344106328fd04963664a60b9bb6496fc25df8e962fcdc1367285fb9/fsspec-2026.7.0-py3-none-any.whl
+  name: fsspec
+  version: 2026.7.0
+  sha256: b57ddbafedfaef7018c1ecab32aa200a9d7ca26b77965f64e48b70061249d279
+  requires_dist:
+  - adlfs ; extra == 'abfs'
+  - adlfs ; extra == 'adl'
+  - pyarrow>=1 ; extra == 'arrow'
+  - dask ; extra == 'dask'
+  - distributed ; extra == 'dask'
+  - pre-commit ; extra == 'dev'
+  - ruff>=0.5 ; extra == 'dev'
+  - numpydoc ; extra == 'doc'
+  - sphinx ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - sphinx-rtd-theme ; extra == 'doc'
+  - yarl ; extra == 'doc'
+  - dropbox ; extra == 'dropbox'
+  - dropboxdrivefs ; extra == 'dropbox'
+  - requests ; extra == 'dropbox'
+  - adlfs ; extra == 'full'
+  - aiohttp!=4.0.0a0,!=4.0.0a1 ; extra == 'full'
+  - dask ; extra == 'full'
+  - distributed ; extra == 'full'
+  - dropbox ; extra == 'full'
+  - dropboxdrivefs ; extra == 'full'
+  - fusepy ; extra == 'full'
+  - gcsfs>=2026.4.0 ; extra == 'full'
+  - libarchive-c ; extra == 'full'
+  - ocifs ; extra == 'full'
+  - panel ; extra == 'full'
+  - paramiko ; extra == 'full'
+  - pyarrow>=1 ; extra == 'full'
+  - pygit2 ; extra == 'full'
+  - requests ; extra == 'full'
+  - s3fs>=2026.6.0 ; extra == 'full'
+  - smbprotocol ; extra == 'full'
+  - tqdm ; extra == 'full'
+  - fusepy ; extra == 'fuse'
+  - gcsfs>=2026.4.0 ; extra == 'gcs'
+  - pygit2 ; extra == 'git'
+  - requests ; extra == 'github'
+  - gcsfs>=2026.4.0 ; extra == 'gs'
+  - panel ; extra == 'gui'
+  - pyarrow>=1 ; extra == 'hdfs'
+  - aiohttp!=4.0.0a0,!=4.0.0a1 ; extra == 'http'
+  - libarchive-c ; extra == 'libarchive'
+  - ocifs ; extra == 'oci'
+  - s3fs>=2026.6.0 ; extra == 's3'
+  - paramiko ; extra == 'sftp'
+  - smbprotocol ; extra == 'smb'
+  - paramiko ; extra == 'ssh'
+  - aiohttp!=4.0.0a0,!=4.0.0a1 ; extra == 'test'
+  - numpy ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-asyncio!=0.22.0 ; extra == 'test'
+  - pytest-benchmark ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-mock ; extra == 'test'
+  - pytest-recording ; extra == 'test'
+  - pytest-rerunfailures ; extra == 'test'
+  - requests ; extra == 'test'
+  - aiobotocore>=2.5.4,<3.0.0 ; extra == 'test-downstream'
+  - dask[dataframe,test] ; extra == 'test-downstream'
+  - moto[server]>4,<5 ; extra == 'test-downstream'
+  - pytest-timeout ; extra == 'test-downstream'
+  - xarray ; extra == 'test-downstream'
+  - adlfs ; extra == 'test-full'
+  - aiohttp!=4.0.0a0,!=4.0.0a1 ; extra == 'test-full'
+  - backports-zstd ; python_full_version < '3.14' and extra == 'test-full'
+  - cloudpickle ; extra == 'test-full'
+  - dask ; extra == 'test-full'
+  - distributed ; extra == 'test-full'
+  - dropbox ; extra == 'test-full'
+  - dropboxdrivefs ; extra == 'test-full'
+  - fastparquet ; extra == 'test-full'
+  - fusepy ; extra == 'test-full'
+  - gcsfs>=2026.4.0 ; extra == 'test-full'
+  - jinja2 ; extra == 'test-full'
+  - kerchunk ; extra == 'test-full'
+  - libarchive-c ; extra == 'test-full'
+  - lz4 ; extra == 'test-full'
+  - notebook ; extra == 'test-full'
+  - numpy ; extra == 'test-full'
+  - ocifs ; extra == 'test-full'
+  - pandas<3.0.0 ; extra == 'test-full'
+  - panel ; extra == 'test-full'
+  - paramiko ; extra == 'test-full'
+  - pyarrow>=1 ; extra == 'test-full'
+  - pyftpdlib ; extra == 'test-full'
+  - pygit2 ; extra == 'test-full'
+  - pytest ; extra == 'test-full'
+  - pytest-asyncio!=0.22.0 ; extra == 'test-full'
+  - pytest-benchmark ; extra == 'test-full'
+  - pytest-cov ; extra == 'test-full'
+  - pytest-mock ; extra == 'test-full'
+  - pytest-recording ; extra == 'test-full'
+  - pytest-rerunfailures ; extra == 'test-full'
+  - python-snappy ; extra == 'test-full'
+  - requests ; extra == 'test-full'
+  - s3fs>=2026.6.0 ; extra == 'test-full'
+  - smbprotocol ; extra == 'test-full'
+  - tqdm ; extra == 'test-full'
+  - urllib3 ; extra == 'test-full'
+  - zarr<3.2.0 ; extra == 'test-full'
+  - zstandard ; python_full_version < '3.14' and extra == 'test-full'
+  - tqdm ; extra == 'tqdm'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/fe/73/dfe1781a2f752dbb25bd7abcc0f5a7ed74f7df3c78c4d53db32a591b01cd/jax_cuda12_pjrt-0.11.0-py3-none-manylinux_2_27_x86_64.whl
   name: jax-cuda12-pjrt
-  version: 0.10.1
-  sha256: 4c50a469f1b7c2fbba278d5b6932fe33de41f833b333cae28151422ec456857d
-- pypi: https://files.pythonhosted.org/packages/fb/63/68f5d0ea81e167db5f59ddb94dc6f837667062113feff1c73fabf8907061/msgpack-1.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
-  name: msgpack
-  version: 1.2.0
-  sha256: a186027e4279efa4c8bf06ce30605498d7d0d3af0fba0b9799dce85a3fd4a93c
+  version: 0.11.0
+  sha256: 242df99c90827a2937df1c444983d4076173f9836dc0bc1d20df86960144f35a
+- pypi: https://files.pythonhosted.org/packages/fe/ed/f1831681fce0e3242346e5458486003c5f124ed69e5e0b847fd029db4973/websockets-16.1.1-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
+  name: websockets
+  version: 16.1.1
+  sha256: 0f62863e8a00a6d33c3d6566ec0b89f23787b747ffe0c3bc71ec0e76b82c94b1
   requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/ff/9a/9afaade874b2fa6c752c36f1548f718b5b83af81ed9b76628329dab81c1b/rfc3986-2.0.0-py2.py3-none-any.whl
   name: rfc3986
@@ -7061,3 +8358,23 @@ packages:
   requires_dist:
   - idna ; extra == 'idna2008'
   requires_python: '>=3.7'
+- pypi: https://files.pythonhosted.org/packages/ff/9c/54ed5abef4caca7e4b7e5bb3faec0bc371d3853788ccd6a1d4736f3fabe1/splax-0.1.0-py3-none-any.whl
+  name: splax
+  version: 0.1.0
+  sha256: 57a26f655c8d2a795e5d023345a83748a29ae4349ce5d92127ef5ffb1f1971b4
+  requires_dist:
+  - jax[cuda]>=0.10
+  - imageio
+  - plyfile
+  - warp-lang>=1.14.0
+  - viser>=1.0 ; extra == 'viewer'
+  - splax[viewer] ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-markdown-docs ; extra == 'tests'
+  - torch>=2.8,<2.11 ; extra == 'tests'
+  - gsplat>=1.5 ; extra == 'tests'
+  - optax>=0.2.8 ; extra == 'tests'
+  - dm-pix>=0.4.5 ; extra == 'tests'
+  - pycolmap>=4.1 ; extra == 'tests'
+  - matplotlib ; extra == 'tests'
+  requires_python: '>=3.11'
diff --git a/properdocs.yml b/properdocs.yml
index be15da8..abedf78 100644
--- a/properdocs.yml
+++ b/properdocs.yml
@@ -64,6 +64,7 @@ nav:
       - JIT compilation: user-guide/control/jit.md
     - Pipelines: user-guide/pipelines.md
     - Visualization: user-guide/visualization.md
+    - Gaussian Splat Rendering: user-guide/splats.md
     - MuJoCo Integration: user-guide/mujoco.md
     - Gymnasium Environments: user-guide/gymnasium-envs.md
   - Examples: examples/index.md
diff --git a/pyproject.toml b/pyproject.toml
index 321d33e..648fd5d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ requires-python = ">=3.11,<3.14" # MuJoCo has no wheels for python 14
 [project.optional-dependencies]
 gpu = ["jax[cuda12]"]
 benchmark = ["fire", "matplotlib", "pandas", "pyinstrument"]
+splats = ["splax[viewer]>=0.1.0"]
 
 [project.urls]
 Homepage = "https://github.com/learnsyslab/crazyflow"
@@ -158,6 +159,10 @@ ruff = "*"
 [tool.pixi.pypi-dependencies]
 crazyflow = { path = "./", editable = true }
 
+# splax requires CUDA jax, which has no osx-arm64 wheels, so splats are linux-only by default
+[tool.pixi.target.linux-64.pypi-dependencies]
+splax = { version = ">=0.1.0", extras = ["viewer"] }
+
 [tool.pixi.feature.gpu]
 platforms = ["linux-64"]
 
diff --git a/tests/conftest.py b/tests/conftest.py
index 9f9064b..e061159 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,11 +1,14 @@
 import os
 
 os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
+os.environ["SCIPY_ARRAY_API"] = "1"
 
 import jax
 import pytest
 
-jax.config.update("jax_compilation_cache_dir", "/tmp/jax_cache")
+# The cache dir is per-user. A shared dir like /tmp/jax_cache breaks on multi-user machines, since
+# jax hard-fails on GPU autotune cache writes when another user owns the directory.
+jax.config.update("jax_compilation_cache_dir", f"/tmp/jax_cache-{os.getuid()}")
 jax.config.update("jax_persistent_cache_min_entry_size_bytes", -1)
 jax.config.update("jax_persistent_cache_min_compile_time_secs", 0)
 # Do not enable XLA caches, crashes PyTest
diff --git a/tests/integration/test_examples.py b/tests/integration/test_examples.py
index dde6b67..e9b0808 100644
--- a/tests/integration/test_examples.py
+++ b/tests/integration/test_examples.py
@@ -4,14 +4,33 @@
 from unittest.mock import patch
 
 import pytest
+from conftest import available_backends
 
-EXAMPLES_DIR = Path(__file__).resolve().parent.parent.parent / "examples"
-# Make example_scripts a list of strings instead of Path objects so that pytest can use it in its
-# automatic printouts. We convert the elements back to Paths in the test function.
-example_scripts = [str(p) for p in sorted(EXAMPLES_DIR.rglob("*.py"))]
+EXAMPLES_DIR = Path(__file__).resolve().parents[2] / "examples"
 
+# Examples that render splats. splax rasterizes with cuda, so these need splax and a GPU.
+REQUIRES_SPLAX = (
+    "rendering/splat_camera.py",
+    "rendering/splat_depth.py",
+    "rendering/splat_gradients.py",
+    "rendering/splat_viewer.py",
+)
 
-@pytest.mark.parametrize("example_script", [str(p) for p in example_scripts])
+requires_splax = pytest.mark.skipif(
+    importlib.util.find_spec("splax") is None or "gpu" not in available_backends(),
+    reason="requires splax and a CUDA GPU",
+)
+
+assert all((EXAMPLES_DIR / name).is_file() for name in REQUIRES_SPLAX), "stale REQUIRES_SPLAX entry"
+
+example_scripts = []
+for path in sorted(EXAMPLES_DIR.rglob("*.py")):
+    marks = requires_splax if path.relative_to(EXAMPLES_DIR).as_posix() in REQUIRES_SPLAX else ()
+    # Parametrize over strings so that pytest prints readable ids. The test converts them back.
+    example_scripts.append(pytest.param(str(path), marks=marks))
+
+
+@pytest.mark.parametrize("example_script", example_scripts)
 @pytest.mark.timeout(60)
 @pytest.mark.integration
 def test_example_main(example_script: str):
diff --git a/tests/unit/test_sensors.py b/tests/unit/test_sensors.py
index e8e1e89..30c0b8b 100644
--- a/tests/unit/test_sensors.py
+++ b/tests/unit/test_sensors.py
@@ -8,7 +8,7 @@
 import pytest
 
 from crazyflow.sim import Sim
-from crazyflow.sim.sensors import _camera_rays, build_render_depth_fn, render_depth
+from crazyflow.sim.sensors.depth import _camera_rays, build_render_depth_fn, render_depth
 
 
 @pytest.mark.unit
diff --git a/tests/unit/test_splat.py b/tests/unit/test_splat.py
new file mode 100644
index 0000000..ee544ce
--- /dev/null
+++ b/tests/unit/test_splat.py
@@ -0,0 +1,295 @@
+"""Unit tests for the gaussian splat module."""
+
+from __future__ import annotations
+
+from functools import partial
+from typing import TYPE_CHECKING
+
+import jax
+import numpy as np
+import pytest
+from conftest import available_backends
+from scipy.spatial.transform import Rotation as R
+
+splax = pytest.importorskip("splax", reason="the splat modules require the optional splats extra")
+
+from crazyflow.sim import Sim  # noqa: E402
+from crazyflow.sim.sensors.splat import (  # noqa: E402
+    build_render_splat_fn,
+    build_render_splat_rgbd_fn,
+    camera_intrinsics,
+    render_splat_rgb,
+    render_splat_rgbd,
+    viewmats,
+)
+from crazyflow.sim.splat import (  # noqa: E402
+    SPLAT_KEYS,
+    SPLAT_SLICES_KEY,
+    SplatViewer,
+    attach_splats,
+)
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+requires_gpu = pytest.mark.skipif("gpu" not in available_backends(), reason="splax requires CUDA")
+
+
+def _write_splat(path: Path, n: int = 64, extent: float = 0.5):
+    """Write a small synthetic splat to a .ply file."""
+    rng = np.random.default_rng(0)
+    means = rng.uniform(-extent, extent, (n, 3)).astype(np.float32)
+    log_scales = np.full((n, 3), np.log(0.05), np.float32)
+    quats = np.tile(np.array([1.0, 0.0, 0.0, 0.0], np.float32), (n, 1))
+    sh_colors = rng.uniform(0.2, 0.8, (n, 3)).astype(np.float32)
+    logit_opacities = np.full((n,), 2.0, np.float32)
+    splax.io.write_ply(path, means, log_scales, quats, sh_colors, logit_opacities)
+
+
+@pytest.mark.unit
+def test_viewmats():
+    # A camera at the origin with identity cam_xmat looks along -z (MuJoCo/OpenGL convention). In
+    # the OpenCV convention splax expects, that point must land at +z, and world +y (up in the GL
+    # camera frame) at -y.
+    vm = viewmats(np.zeros((1, 3)), np.eye(3)[None])
+    assert vm.shape == (1, 4, 4)
+    assert np.allclose(vm[0] @ np.array([0.0, 0.0, -2.0, 1.0]), [0.0, 0.0, 2.0, 1.0], atol=1e-6)
+    assert np.allclose(vm[0] @ np.array([0.0, 1.0, -2.0, 1.0]), [0.0, -1.0, 2.0, 1.0], atol=1e-6)
+    # Random camera pose: rotation stays orthonormal with det +1, camera center maps to the origin
+    xpos, xmat = np.random.default_rng(1).normal(size=(1, 3)), R.random().as_matrix()[None]
+    vm = viewmats(xpos, xmat)
+    rot = vm[0, :3, :3]
+    assert np.allclose(rot @ rot.T, np.eye(3), atol=1e-6)
+    assert np.isclose(np.linalg.det(rot), 1.0)
+    assert np.allclose(vm[0] @ np.append(xpos[0], 1.0), [0.0, 0.0, 0.0, 1.0], atol=1e-6)
+
+
+@pytest.mark.unit
+def test_camera_intrinsics():
+    sim = Sim()
+    width, height = 320, 240
+    f, c = camera_intrinsics(sim.mj_model, 0, (width, height))
+    fov_y = np.deg2rad(sim.mj_model.cam_fovy[0])
+    assert np.isclose(f[0], f[1]), "Pixels must be square"
+    assert np.isclose(f[1], height / 2 / np.tan(fov_y / 2))
+    assert c == (width / 2, height / 2)
+
+
+@pytest.mark.unit
+def test_attach_splats(tmp_path: Path):
+    n_splats = 64
+    _write_splat(tmp_path / "splat.ply", n=n_splats)
+    sim = Sim(n_worlds=2, n_drones=2)
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    # The scene and both drone copies concatenate into one buffer per parameter array
+    n = 3 * n_splats
+    shapes = ((n, 3), (n, 3), (n, 4), (n, 3), (n,))
+    for key, shape in zip(SPLAT_KEYS, shapes):
+        assert key in sim.data.plugins, f"Missing plugin key {key}"
+        assert sim.data.plugins[key].shape == shape
+        assert sim.data.plugins[key].device == sim.device
+    slices = sim.data.plugins[SPLAT_SLICES_KEY]
+    assert np.array_equal(slices, [[n_splats, 2 * n_splats], [2 * n_splats, 3 * n_splats]])
+    # Splat data must survive resets
+    sim.reset()
+    assert all(key in sim.data.plugins for key in SPLAT_KEYS)
+    for key, shape in zip(SPLAT_KEYS, shapes):
+        assert sim.data.plugins[key].shape == shape
+    assert sim.data.plugins[SPLAT_SLICES_KEY].shape == (2, 2)
+
+
+@pytest.mark.unit
+def test_attach_splats_scene_only(tmp_path: Path):
+    n_splats = 64
+    _write_splat(tmp_path / "splat.ply", n=n_splats)
+    sim = Sim(n_drones=2)
+    attach_splats(sim, scene=tmp_path / "splat.ply")
+    assert sim.data.plugins[SPLAT_KEYS[0]].shape == (n_splats, 3)
+    assert sim.data.plugins[SPLAT_SLICES_KEY].shape == (0, 2)
+
+
+@pytest.mark.unit
+def test_attach_splats_no_input():
+    sim = Sim()
+    with pytest.raises(ValueError, match="scene or drone"):
+        attach_splats(sim)
+
+
+@pytest.mark.unit
+def test_render_splat_before_attach():
+    sim = Sim()
+    with pytest.raises(RuntimeError, match="attach_splats"):
+        render_splat_rgb(sim)
+    with pytest.raises(RuntimeError, match="attach_splats"):
+        build_render_splat_fn(sim)
+    with pytest.raises(RuntimeError, match="attach_splats"):
+        render_splat_rgbd(sim)
+    with pytest.raises(RuntimeError, match="attach_splats"):
+        build_render_splat_rgbd_fn(sim)
+    with pytest.raises(RuntimeError, match="attach_splats"):
+        SplatViewer(sim)
+
+
+@pytest.mark.unit
+def test_render_splat_requires_gpu(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply")
+    sim = Sim(device="cpu")
+    attach_splats(sim, drone=tmp_path / "splat.ply")
+    with pytest.raises(RuntimeError, match="GPU"):
+        render_splat_rgb(sim)
+    with pytest.raises(RuntimeError, match="GPU"):
+        render_splat_rgbd(sim)
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_render_splat_rgb(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(n_worlds=2, n_drones=2, device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    # Every drone's fpv camera renders into a (n_worlds, n_drones, H, W, 3) stack
+    img = render_splat_rgb(sim, resolution=(32, 24))
+    assert img.shape == (2, 2, 24, 32, 3)
+    assert np.all(np.isfinite(img))
+    assert img.max() > 0.0, "Nothing is visible in the image"
+    # Selecting a single drone matches that slice of the full stack
+    one = render_splat_rgb(sim, drones=1, resolution=(32, 24))
+    assert one.shape == (2, 1, 24, 32, 3)
+    assert np.allclose(one[:, 0], img[:, 1], atol=1e-5)
+    # The compiled variant renders the same images
+    render_fn = build_render_splat_fn(sim, resolution=(32, 24))
+    assert np.allclose(img, render_fn(sim.data), atol=1e-5)
+    # Hiding each drone from its own camera changes the images
+    excl = render_splat_rgb(sim, resolution=(32, 24), exclude_self=True)
+    assert not np.allclose(img, excl)
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_render_splat_camera_prefix(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(n_worlds=2, n_drones=2, device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    fpv = render_splat_rgb(sim, resolution=(32, 24))
+    track = render_splat_rgb(sim, resolution=(32, 24), camera_prefix="track_cam")
+    assert track.shape == fpv.shape
+    assert not np.allclose(fpv, track), "track_cam and fpv_cam must give different views"
+    # The builder fuses the prefix in and matches the direct call
+    render_fn = build_render_splat_fn(sim, resolution=(32, 24), camera_prefix="track_cam")
+    assert np.allclose(track, render_fn(sim.data), atol=1e-5)
+    # An unknown prefix raises
+    with pytest.raises(ValueError, match="not found"):
+        render_splat_rgb(sim, resolution=(32, 24), camera_prefix="does_not_exist")
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_render_splat_rgbd(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(n_worlds=2, n_drones=2, device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    # Every drone's fpv camera renders into a (n_worlds, n_drones, H, W, 4) rgbd stack
+    img = render_splat_rgbd(sim, resolution=(32, 24), max_range=7.0)
+    assert img.shape == (2, 2, 24, 32, 4)
+    assert np.all(np.isfinite(img))
+    depth = img[..., 3]
+    assert depth.max() <= 7.0, "Depth reaches past the sensor range"
+    assert depth.min() < 7.0, "Nothing is visible in the depth image"
+    # Depth is metric, so it leaves the unit interval the color channels are confined to
+    assert depth[depth < 7.0].max() > 1.0
+    # The color channels match the dedicated rgb sensor approximately
+    assert np.allclose(img[..., :3], render_splat_rgb(sim, resolution=(32, 24)), atol=1e-5)
+    # Selecting a single drone keeps the drone axis and matches that slice of the full stack
+    one = render_splat_rgbd(sim, drones=1, resolution=(32, 24), max_range=7.0)
+    assert one.shape == (2, 1, 24, 32, 4)
+    assert np.allclose(one[:, 0], img[:, 1], atol=1e-5)
+    # The compiled variant renders the same images
+    render_fn = build_render_splat_rgbd_fn(sim, resolution=(32, 24), max_range=7.0)
+    assert np.allclose(img, render_fn(sim.data), atol=1e-5)
+    # Hiding each drone from its own camera changes the images
+    excl = render_splat_rgbd(sim, resolution=(32, 24), max_range=7.0, exclude_self=True)
+    assert not np.allclose(img, excl)
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_render_splat_rgbd_range(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    render = partial(render_splat_rgbd, sim, resolution=(32, 24), max_range=7.0)
+    # Demanding more coverage can only reject hits, never create them, so nothing moves closer
+    lenient, strict = render(alpha_threshold=0.1)[..., 3], render(alpha_threshold=0.9)[..., 3]
+    assert np.all(strict >= lenient - 1e-5)
+    assert np.any(strict > lenient), "The threshold never rejected a pixel"
+    # No coverage can reach a threshold above one, leaving an empty image at the sensor range
+    assert np.all(render(alpha_threshold=1.1)[..., 3] == 7.0)
+    # Hits are clipped to the range as well, not just the pixels that miss
+    near = render_splat_rgbd(sim, resolution=(32, 24), max_range=1e-3)
+    assert np.all(near[..., 3] == 1e-3)
+    # Only the depth channel is gated, the colors are untouched by the sensor range
+    assert np.allclose(near[..., :3], render()[..., :3], atol=1e-5)
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_build_render_splat_fn(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(n_worlds=2, n_drones=2, device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    # The renderer is a pure function of the simulation data, so it traces and jits
+    render_fn = build_render_splat_fn(sim, resolution=(32, 24))
+    assert np.allclose(render_fn(sim.data), jax.jit(render_fn)(sim.data), atol=1e-5)
+    # Rebuilding is only an optimization, the images match the one-shot renderer
+    assert np.allclose(render_fn(sim.data), render_splat_rgb(sim, resolution=(32, 24)), atol=1e-5)
+    # The renderer reads the poses per call instead of baking in the poses it was built with
+    before = render_fn(sim.data)
+    sim.step(sim.freq // 10)
+    assert not np.allclose(before, render_fn(sim.data))
+    # The drone selection and self-exclusion carry through the builder
+    one = build_render_splat_fn(sim, resolution=(32, 24), drones=1)(sim.data)
+    assert one.shape == (2, 1, 24, 32, 3)
+    assert np.allclose(one, render_splat_rgb(sim, drones=1, resolution=(32, 24)), atol=1e-5)
+    excl = build_render_splat_fn(sim, resolution=(32, 24), exclude_self=True)(sim.data)
+    assert np.allclose(excl, render_splat_rgb(sim, resolution=(32, 24), exclude_self=True), 1e-5)
+    assert not np.allclose(excl, render_fn(sim.data))
+
+
+@pytest.mark.unit
+@requires_gpu
+def test_build_render_splat_rgbd_fn(tmp_path: Path):
+    _write_splat(tmp_path / "splat.ply", extent=1.0)
+    sim = Sim(n_worlds=2, n_drones=2, device="gpu")
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    render_fn = build_render_splat_rgbd_fn(sim, resolution=(32, 24), max_range=7.0)
+    assert np.allclose(render_fn(sim.data), jax.jit(render_fn)(sim.data), atol=1e-5)
+    direct = partial(render_splat_rgbd, sim, resolution=(32, 24), max_range=7.0)
+    assert np.allclose(render_fn(sim.data), direct(), atol=1e-5)
+    before = render_fn(sim.data)
+    sim.step(sim.freq // 10)
+    assert not np.allclose(before, render_fn(sim.data))
+    # The sensor range and the coverage threshold carry through the builder
+    render_fn = build_render_splat_rgbd_fn(
+        sim, resolution=(32, 24), max_range=7.0, alpha_threshold=1.1
+    )
+    assert np.all(render_fn(sim.data)[..., 3] == 7.0)
+    render_fn = build_render_splat_rgbd_fn(
+        sim, resolution=(32, 24), max_range=7.0, drones=1, exclude_self=True
+    )
+    one = render_fn(sim.data)
+    assert one.shape == (2, 1, 24, 32, 4)
+    assert np.allclose(one, direct(drones=1, exclude_self=True), atol=1e-5)
+
+
+@pytest.mark.unit
+def test_splat_viewer(tmp_path: Path):
+    pytest.importorskip("viser")
+
+    _write_splat(tmp_path / "splat.ply")
+    sim = Sim(n_drones=2)
+    attach_splats(sim, scene=tmp_path / "splat.ply", drone=tmp_path / "splat.ply")
+    viewer = SplatViewer(sim)
+    sim.step()
+    viewer.update(sim)
+    viewer.close()
+    sim.close()