Scene completion and forecasting are two popular perception problems in research for mobile agents like autonomous vehicles. Existing approaches treat the two problems in isolation, resulting in a separate perception of the two aspects. In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework. This task requires new algorithms to address three challenges altogether: (1) sparse-to-dense reconstruction, (2) partial-to-complete hallucination, and (3) 3D-to-4D prediction. To enable supervision and evaluation, we curate a large-scale dataset termed OCFBench from public autonomous driving datasets. We analyze the performance of closely related existing baseline models and our own ones on our dataset. We envision that this research will inspire and call for further investigation in this evolving and crucial area of 4D perception. Our code for data curation and baseline implementation is available at https://github.com/ai4ce/Occ4cast.

本文通过介绍新颖的无人驾驶LiDAR感知任务OCF，将场景完整和预测问题统一为一个相互关联的框架，解决了稀疏到稠密重建、局部到完整幻觉和3D到4D预测等三个挑战，并分析了现有模型在OCFBench数据集上的表现，为进一步深入研究这一关键的四维感知领域提供了启发。

基于LiDAR的4D占位完成和预测