  3D occupancy perception technology aims to observe and understand dense 3D
environments for autonomous vehicles. Owing to its comprehensive perception
capability, this technology is emerging as a trend in autonomous driving
perception systems, and is attracting significant attention from both industry
and academia. Similar to traditional bird's-eye view (BEV) perception, 3D
occupancy perception has the nature of multi-source input and the necessity for
information fusion. However, the difference is that it captures vertical
structures that are ignored by 2D BEV. In this survey, we review the most
recent works on 3D occupancy perception, and provide in-depth analyses of
methodologies with various input modalities. Specifically, we summarize general
network pipelines, highlight information fusion techniques, and discuss
effective network training. We evaluate and analyze the occupancy perception
performance of the state-of-the-art on the most popular datasets. Furthermore,
challenges and future research directions are discussed. We hope this paper
will inspire the community and encourage more research work on 3D occupancy
perception. A comprehensive list of studies in this survey is publicly
available in an active repository that continuously collects the latest work:
https://github.com/HuaiyuanXu/3D-Occupancy-Perception.
