发表机构
Hunan University; National Engineering Research Center of Robot Visual Perception and Control Technology（湖南大学; 国家机器人视觉感知与控制技术工程研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SelectOccFlow，通过语义引导采样、状态条件时间聚合和范围感知空间聚合，选择性细化图像、时间与体素域证据，提升相机占据与场景流预测的鲁棒性，在OpenOcc上达到SOTA OccScore 44.9。
AI 中文摘要
自动驾驶的全面3D场景理解需要建模几何、语义和运动。然而，基于相机的占据和场景流预测对不可靠的空间和时间聚合敏感，这些聚合由语义不兼容的图像特征、错位的历史观测和不完整的体素结构引起。为解决此问题，我们提出SelectOccFlow，一个选择性时空聚合框架，逐步在图像、时间和体素域中细化上下文证据。为获得语义兼容的图像证据，我们设计语义引导采样（SGS）以利用语义先验调节特征采样。由于仅可靠的图像证据无法解决时间不一致性，我们随后提出状态条件时间聚合（SCTA），根据体素状态选择性检索历史证据。为进一步增强体素表示的结构完整性，我们引入范围感知空间聚合（ESA），利用方向结构支持细化前景几何。在OpenOcc上的实验表明，SelectOccFlow达到最先进的OccScore 44.9，比之前最佳提高+4.2%。它还在Occ3D-nus上保持竞争力的占据性能，并在nuScenes-C损坏下将平均OccScore提高+11.1%，展示了对视觉损坏的改进鲁棒性。源代码将在此https URL公开提供。
英文摘要
Comprehensive 3D scene understanding for autonomous driving requires modeling geometry, semantics, and motion. However, camera-based occupancy and scene flow prediction are sensitive to unreliable spatial and temporal aggregation, caused by semantically incompatible image features, misaligned historical observations, and incomplete voxel structures. To address this issue, we propose SelectOccFlow, a selective spatiotemporal aggregation framework that progressively refines contextual evidence across image, temporal, and voxel domains. To obtain semantically compatible image evidence, we design Semantic-Guided Sampling (SGS) to regulate feature sampling with semantic priors. Since reliable image evidence alone cannot resolve temporal inconsistency, we then present State-Conditioned Temporal Aggregation (SCTA) to selectively retrieve historical evidence according to voxel states. To further enhance the structural completeness of voxel representations, we introduce Extent-Aware Spatial Aggregation (ESA), which exploits directional structural support to refine foreground geometry. Experiments on OpenOcc demonstrate that SelectOccFlow achieves a state-of-the-art OccScore of 44.9, improving the previous best by +4.2%. It also maintains competitive occupancy performance on Occ3D-nus and improves the mean OccScore under nuScenes-C corruptions by +11.1%, demonstrating improved robustness to visual corruptions. The source code will be made publicly available at https://github.com/muchen1021/SelectOccFlow.
Comments9 pages, 4 figures
