OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera

Abstract

Robust 3D semantic occupancy is essential for legged and humanoid robots, yet most Semantic Scene Completion (SSC) systems are built for wheeled platforms with forward-facing sensors. We present OneOcc, a vision-only panoramic SSC framework tailored to severe body jitter and 360deg continuity. OneOcc integrates four complementary modules: (i) Dual-Projection fusion (DP-ER), which jointly exploits the raw annular panorama and its equirectangular unfolding to preserve true 360deg continuity while enabling grid-aligned feature extraction and seam-aware context; (ii) Bi-Grid Voxelization (BGV), which reasons in Cartesian and polar/cylindrical voxel spaces to reduce discretization bias and better align with panoramic geometry, yielding sharper free/occupied boundaries; (iii) a lightweight decoder with Hierarchical AMoE-3D fusion that dynamically routes multi-scale 3D features to specialized experts, improving long-range context and occlusion handling; and (iv) a plug-and-play Gait Displacement Compensation (GDC) module that learns feature-level motion correction from gait, stabilizing representations without extra sensors. We also release two panoramic occupancy benchmarks: QuadOcc (real quadruped, first-person 360deg) and Human360Occ (H3O) (CARLA human-ego 360deg with RGB/Depth/semantic-occupancy and standardized within-/cross-city splits). OneOcc sets new SOTA: on QuadOcc it exceeds strong vision baselines and even popular LiDAR methods, and on H3O it improves within-city by +3.83 mIoU and cross-city by +8.08. The modules are lightweight, enabling deployable full-surround semantic perception for legged and humanoid robots.