LiDARを用いた4D占有補完と予測
LiDAR-based 4D Occupancy Completion and Forecasting
自動運転向けに、LiDARの占有情報を補完し将来予測する新タスクOCFを提案し、大規模データセットOCFBenchとベースラインを構築した論文。
著者: Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, Chen Feng
分類: cs.CV, cs.RO
原文アブストラクト
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.
