基于 LiDAR 的 4D 占据补全与预测
LiDAR-based 4D Occupancy Completion and Forecasting
AI总结：
提出统一场景补全与预测的 LiDAR 4D 占据任务 OCF，构建 OCFBench 数据集并评估相关及自有基线模型。
摘要：
场景补全和预测是自动驾驶汽车等移动智能体研究中两个常见的感知问题。现有方法将这两个问题孤立处理，导致对这两个方面产生割裂的感知。本文在自动驾驶背景下提出一项名为占据补全与预测（OCF）的新型 LiDAR 感知任务，以将这些方面统一到一个连贯的框架中。该任务需要新算法同时应对三项挑战：（1）从稀疏到稠密的重建；（2）从局部到完整的幻构；（3）从 3D 到 4D 的预测。为实现监督与评估，我们基于公开自动驾驶数据集整理了一个名为 OCFBench 的大规模数据集。我们在该数据集上分析了密切相关的现有基线模型以及我们自有模型的性能。我们展望，这项研究将启发并呼吁在 4D 感知这一不断发展且至关重要的领域开展进一步研究。我们用于数据整理和基线实现的代码可在 https://github.com/ai4ce/Occ4cast 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
