{"ID":2845856,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03571","arxiv_id":"2511.03571","title":"OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera","abstract":"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.","short_abstract":"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-Proj...","url_abs":"https://arxiv.org/abs/2511.03571","url_pdf":"https://arxiv.org/pdf/2511.03571v2","authors":"[\"Hao Shi\",\"Ze Wang\",\"Shangwei Guo\",\"Mengfei Duan\",\"Song Wang\",\"Teng Chen\",\"Kailun Yang\",\"Lin Wang\",\"Kaiwei Wang\"]","published":"2025-11-05T15:51:42Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"eess.IV\"]","methods":"[]","has_code":false,"code_links":[{"ID":607390,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2845856,"paper_url":"https://arxiv.org/abs/2511.03571","paper_title":"OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera","repo_url":"https://github.com/MasterHow/OneOcc","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}