SelectOccFlow: 3D占有とシーンフローのための選択的時空間集約
SelectOccFlow: Selective Spatiotemporal Aggregation for 3D Occupancy and Scene Flow Prediction
自動運転向けに、画像・時間・ボクセルの各領域で信頼できる情報だけを選択的に集約し、3D占有とシーンフローを高精度かつ頑健に予測するフレームワークを提案した。
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著者: Yuhang Wang, Kai Luo, Yuanfan Zheng, Kailun Yang
分類: cs.CV, cs.RO
原文アブストラクト
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.