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

Abstract

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. 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. After exploration, cached local features are consolidated as complementary evidence and combined with the completed online representation for multi-scale spatial refinement. On EmbodiedOcc-ScanNet, VEOcc++ achieves 67.12\% IoU and 58.40\% mIoU, establishing new state-of-the-art performance for online incremental mapping. With the local predictor fixed, our online mapping pipeline outperforms VEOcc fusion by 3.70 IoU and 3.74 mIoU points, demonstrating substantial gains from the proposed global mapping design. Offline global map refinement further improves the completed map to 67.79% IoU and 59.37% mIoU using only the observations acquired during exploration. Zero-shot deployment on a mobile robot further demonstrates transferability to unseen environments, onboard feasibility, and scalability to large-scale indoor mapping. Project page: https://wryzju.github.io/VEOccpp/.