LiDAR-based 4D Occupancy Completion and Forecasting
Xinhao Liu Moonjun Gong Qi Fang Haoyu Xie Yiming Li Hang Zhao Chen Feng
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.