CrowdOcc: Monocular Semantic Scene Completion for Quadruped Robots in Crowded Indoor Environments

Abstract:Monocular semantic scene completion (SSC) for quadruped robots remains underexplored in real crowded indoor environments, where human-scene occlusion disrupts static geometry and human occupancy predictions are often incomplete or spatially misplaced. We present CrowdOcc, an RGB-D dataset and monocular SSC framework for this setting. CrowdOcc contains 25.1K frames from 11 indoor scenes, with semantic occupancy annotations constructed through static dynamic decoupling. Our framework combines: (i) Normal Guided Scene Geometry Fusion (NGSGF) to complement depth-aware lifting with surface-normal cues for occlusion robust geometry; and (ii) Human-Centric Sparse Interaction (HCSI) to selectively model human-human and local human scene relations in 3D. Our method achieves state-of-the-art SSC performance on CrowdOcc's scene-disjoint test set, reaching 15.80 IoU, 11.40 mIoU, and 46.23 Human IoU, demonstrating generalization to unseen indoor scenes.

8 pages, 4 figures. Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2027

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2610.03031 [cs.CV]

(or arXiv:2610.03031v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.03031 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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