OneOcc: 単一パノラマカメラによる脚式ロボットの意味的占有予測
OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera
脚式ロボット向けに、単一のパノラマカメラ映像から360度の3D意味的占有を予測するフレームワークOneOccを提案。歩行時の揺れ補償や二重投影融合などの工夫で高精度を実現し、新たなベンチマークも公開。
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著者: Hao Shi, Ze Wang, Shangwei Guo, Mengfei Duan, Song Wang, Teng Chen, Kailun Yang, Lin Wang, Kaiwei Wang
分類: cs.RO, cs.CV, eess.IV
原文アブストラクト
Robust 3D semantic occupancy is crucial for legged/humanoid robots, yet most semantic scene completion (SSC) systems target wheeled platforms with forward-facing sensors. We present OneOcc, a vision-only panoramic SSC framework designed for gait-introduced body jitter and 360{\deg} continuity. OneOcc combines: (i) Dual-Projection fusion (DP-ER) to exploit the annular panorama and its equirectangular unfolding, preserving 360{\deg} continuity and grid alignment; (ii) Bi-Grid Voxelization (BGV) to reason in Cartesian and cylindrical-polar spaces, reducing discretization bias and sharpening free/occupied boundaries; (iii) a lightweight decoder with Hierarchical AMoE-3D for dynamic multi-scale fusion and better long-range/occlusion reasoning; and (iv) plug-and-play Gait Displacement Compensation (GDC) learning feature-level motion correction without extra sensors. We also release two panoramic occupancy benchmarks: QuadOcc (real quadruped, first-person 360{\deg}) and Human360Occ (H3O) (CARLA human-ego 360{\deg} with RGB, Depth, semantic occupancy; standardized within-/cross-city splits). OneOcc sets a new state of the art on QuadOcc, outperforming strong vision baselines and remaining competitive with classical LiDAR baselines; on H3O it gains +3.83 mIoU (within-city) and +8.08 (cross-city). Modules are lightweight, enabling deployable full-surround perception for legged/humanoid robots. Datasets and code will be publicly available at https://github.com/MasterHow/OneOcc.
