发表机构
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对仅相机4D占用预测中输入多视图帧间时空建模不足问题，提出InterOCF框架，通过联合建模3D体素表示和多视图分割序列中的时间动态，并纳入2D与3D分支特征交互，实验证明其性能优于现有方法。
AI 中文摘要
仅相机4D占用预测能让自动驾驶车辆仅根据历史多视图图像预测未来3D语义场景，对驾驶安全至关重要。当前方法虽有不错表现，但输入多视图帧间的强时空建模仍未充分探索，限制了4D预测性能。为此引入InterOCF框架，它在3D体素表示和多视图分割序列中联合建模时间动态，明确纳入2D和3D分支间的特征交互。该框架含三个核心组件：3D时空模块、2D时空模块、时空交互建模模块。在多个数据集上的实验表明InterOCF持续优于现有基线方法。
英文摘要
Camera-only 4D occupancy forecasting enables autonomous vehicles to predict future 3D semantic scenes solely from historical multi-view images, which is critical for driving safety. Even though current methods have achieved good performance, the strong spatial-temporal modeling between the input multi-view frames is still underexplored, which limits the performance of those methods in future 4D forecasting. To address this gap, we introduce a novel framework, InterOCF, for 4D occupancy forecasting that jointly models temporal dynamics in both 3D voxel-based representations and multi-view segmentation sequences, while explicitly incorporating feature interaction between the 2D and 3D branches. Our framework incorporates three core components: 1) A 3D Spatio-Temporal (3DST) module that learns volumetric dynamics from historical voxel states to predict future voxel states; 2) A 2D Spatio-Temporal (2DST) module employing an auxiliary multi-view temporal segmentation forecasting task to enhance temporal semantic dynamics; 3) A Spatio-Temporal Interaction Modeling (STIM) module that enables feature interaction between 2D and 3D representations. Experiments on the nuScenes, Lyft-Level5, and nuScenes-Occupancy datasets show that InterOCF consistently outperforms existing baseline approaches.
Comments12 pages, 7 figures
