Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning

• URL: http://arxiv.org/abs/2606.19122v1
• Date: Wed, 17 Jun 2026 14:35:52 GMT
• Title: Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning
• Abstract summary: WalkOCC is a hybrid Ray-marching monocular 3D occupancy perception framework for robots operating on sidewalks.<n>It bootstraps pseudo occupancy supervision from paired sequences and jointly learns image-level representations on additional 2D-only data.<n>It yields stable optimization and improved generalization without requiring costly 3D occupancy annotations.
• Score: 36.24525372937089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe navigation of mobile robots such as delivery bots and electric wheelchairs. Existing occupancy learning pipelines are largely designed for on-road autonomous driving and often train on large-scale paired LiDAR-RGB datasets with dense 3D supervision and multiple camera inputs, which are costly to collect and do not adequately capture sidewalk-specific characteristics. We propose WalkOCC, a hybrid Ray-marching monocular 3D occupancy perception framework for robots operating on sidewalks. WalkOCC explicitly couples geometric grounding from LiDAR-RGB paired data with scalable learning from large-scale unpaired monocular images. It bootstraps pseudo occupancy supervision from paired sequences and jointly learns image-level representations on additional 2D-only data. It yields stable optimization and improved generalization without requiring costly 3D occupancy annotations. Extensive experiments demonstrate consistent gains in prediction accuracy, fine-grained segmentation of subtle urban structures such as curbs and gutters, and robustness to environmental and cross-embodiment shifts compared with self-supervised image-based baselines. To facilitate evaluation and benchmarking, we also introduce Sidewalk3D, a large-scale sidewalk perception dataset with LiDAR-camera paired sequences collected across multiple locations and time periods, along with 3D semantic occupancy annotations for evaluation. Code and data will be made available.

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