OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918388878540800 |
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| author | Shi, Hao Wang, Ze Guo, Shangwei Duan, Mengfei Wang, Song Chen, Teng Yang, Kailun Wang, Lin Wang, Kaiwei |
| author_facet | Shi, Hao Wang, Ze Guo, Shangwei Duan, Mengfei Wang, Song Chen, Teng Yang, Kailun Wang, Lin Wang, Kaiwei |
| contents | 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° continuity. OneOcc combines: (i) Dual-Projection fusion (DP-ER) to exploit the annular panorama and its equirectangular unfolding, preserving 360° 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°) and Human360Occ (H3O) (CARLA human-ego 360° 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_03571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera Shi, Hao Wang, Ze Guo, Shangwei Duan, Mengfei Wang, Song Chen, Teng Yang, Kailun Wang, Lin Wang, Kaiwei Robotics Computer Vision and Pattern Recognition Image and Video Processing 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° continuity. OneOcc combines: (i) Dual-Projection fusion (DP-ER) to exploit the annular panorama and its equirectangular unfolding, preserving 360° 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°) and Human360Occ (H3O) (CARLA human-ego 360° 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. |
| title | OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera |
| topic | Robotics Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2511.03571 |