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.

翻译：场景完成与预测是自主驾驶等移动智能体研究中的两个热门感知问题。现有方法将这两个问题独立处理，导致两方面感知相互割裂。本文提出一种名为"占用完成与预测"（Occupancy Completion and Forecasting, OCF）的新型激光雷达感知任务，将其融入自主驾驶场景的统一框架中。该任务要求新算法同时应对三个挑战：（1）稀疏到密集重建，（2）局部到完整补全，（3）3D到4D预测。为实现监督与评估，我们基于公开自主驾驶数据集整理出大规模数据集OCFBench。我们在此数据集上分析了现有基线模型及自有模型的性能。我们期待这项研究能够激发并推动这一持续演进的关键4D感知领域深入探索。数据整理与基线实现代码已开源至https://github.com/ai4ce/Occ4cast。