Add optional GPU-accelerated panorama depth merge

[Tejas (tejas-130704)]

Problem

The panorama pipeline (moge infer_panorama) spends most of its time in merge_panorama_depth, which stitches the per-view depth predictions into one panorama depth map using scipy.sparse.linalg.lsmr — a CPU-only iterative least-squares solver. On a 12-view panorama this solve took 15-25s per frame in profiling, while GPU inference on the same frame took 5-15s and sat idle the rest of the time. Increasing GPU batch size or switching to TensorRT had no effect, confirming the bottleneck is this CPU solve, not the model.

Fix

Adds an optional device parameter to merge_panorama_depth and a --gpu_merge flag to infer_panorama.py. When unset (default), behavior is unchanged — same lsmr CPU path as before. When a CUDA device is passed, the same sparse system is solved on GPU via a torch-based conjugate gradient loop instead of lsmr. Matrix assembly (csr_array/hstack/vstack) is untouched.

No new dependencies — torch is already required.

Files changed

• moge/utils/panorama.py — merge_panorama_depth gains device: Optional[str] = None; new private _solve_gpu helper.
• moge/scripts/infer_panorama.py — new --gpu_merge flag, passed through to merge_panorama_depth.

Validation

Ran infer_panorama with and without --gpu_merge on the same panoramas and compared:

• Mean/max abs difference between CPU and GPU depth output: <fill in>
• Merge time, CPU vs GPU: <fill in> vs <fill in>
• Visual check of depth map for seam artifacts at view boundaries: <pass/fail>

Compatibility

Default behavior (--gpu_merge unset) is unchanged. This is purely opt-in.

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