VEOcc++: Learning Persistent Voxel Feature Maps for Embodied Online Semantic Occupancy Mapping and Offline Refinement

Key points

• We present VEOcc++, a unified voxel-centric framework that connects both stages through a persistent sparse voxel feature map.1
• During exploration, the map expands with newly observed regions and integrates aligned local features through reliability-guided updates, while context-aware decoding produces the current semantic occupancy map.1
• On EmbodiedOcc-ScanNet, VEOcc++ achieves 67.12% IoU and 58.40% mIoU, establishing new state-of-the-art performance for online incremental mapping.1
• Zero-shot deployment on a mobile robot further demonstrates transferability to unseen environments, onboard feasibility, and scalability to large-scale indoor mapping.1

Sources (1)

• [1]VEOcc++: Learning Persistent Voxel Feature Maps for Embodied Online Semantic Occupancy Mapping and Offline Refinement
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:21 AM

  Semantic occupancy mapping supports embodied perception during exploration and detailed scene understanding afterward, giving rise to two complementary requirements: online incremental mapping for maintaining an up-to-date global map under causal constraints, and offline global map refinement for improving the completed scene representation with additional computation.

  We present VEOcc++, a unified voxel-centric framework that connects both stages through a persistent sparse voxel feature map.

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