VEOcc：面向具身场景理解的体素中心在线语义占用预测
VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
AI总结：
提出一种基于体素的递归感知-同化框架VEOcc，通过时空感知在线更新策略实现无需初始尺度估计的高效、鲁棒语义占用预测，在局部和具身场景中达到最先进性能。
摘要：
对于自主探索至关重要，在线3D占用预测和映射逐步构建密集的空间表示。然而，近期以高斯为中心的方法在结构边界保真度上存在困难，且严重依赖预定义的场景大小先验，从根本上限制了其操作效率。在这项工作中，我们提出了VEOcc，一个以体素为中心的框架，表述为递归感知-同化范式。通过消除初始尺度估计的需要，VEOcc实现了高度精简、开放的地图扩展。此外，为了在离散体素空间内鲁棒地聚合带噪声的时间观测，我们提出了一种时空感知在线更新策略。它集成了跨时间对数聚合（TLA）以保持时间一致性、可靠性感知置信度调制（RCM）以进行空间不确定性校准，以及置信度驱动的增量状态更新（CSU）以实现鲁棒的全局状态同化。在Occ-ScanNet和EmbodiedOcc-ScanNet上的大量实验表明，VEOcc在局部和具身设置中均建立了新的最先进性能，为真实世界探索提供了准确且高效的解决方案。值得注意的是，在自收集视频序列上的零样本评估进一步证实了其在完全未见过的真实世界环境中的鲁棒分布外泛化能力。最终，我们的框架为自主探索提供了准确且高效的解决方案。代码和补充可视化可在我们的项目页面获取：https://wryzju.github.io/VEOcc/。
英文摘要：
Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Embodied online occupancy prediction remains predominantly Gaussian-centric, despite the wide use of voxel representations for frame-wise scene completion. We present VEOcc, to the best of our knowledge, the first voxel-centric framework for online embodied semantic occupancy prediction. It incrementally maintains a sparse global semantic voxel map from monocular observations and enables open-ended expansion without predefined scene bounds. To robustly integrate noisy multi-view predictions, we further introduce a Spatio-Temporal-Aware Online Update Strategy comprising Cross-Temporal Logit Aggregation (TLA) for short-term temporal consistency, Reliability-Aware Confidence Modulation (RCM) for spatial uncertainty calibration, and Confidence-Driven Incremental State Update (CSU) for robust global state assimilation. Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate state-of-the-art performance among models of comparable scale in both local and embodied settings. Moreover, onboard deployment on a mobile robot validates practical online operation and long-horizon scalability, while results on self-collected handheld sequences demonstrate zero-shot generalization to unseen real-world environments. Code and supplementary visualizations are available on our project page: https://wryzju.github.io/VEOcc/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
