自动驾驶占用感知综述：信息融合视角
A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective
AI总结：
该综述从信息融合视角梳理自动驾驶3D占用感知的最新研究，分析不同模态方法、网络流水线与融合技术，评估主流数据集上的SOTA性能，探讨挑战与未来方向，并提供持续更新的研究列表仓库。
摘要：
3D占用感知技术旨在为自动驾驶车辆观测并理解稠密三维环境。凭借其全面的感知能力，该技术正成为自动驾驶感知系统的发展趋势，受到工业界与学术界的高度关注。与传统鸟瞰图（BEV）感知类似，3D占用感知具备多源输入的特性，且存在信息融合的必要性，但不同之处在于它能捕捉二维鸟瞰图所忽略的垂直结构。本综述梳理了3D占用感知领域的最新研究成果，针对不同输入模态的方法展开深入分析。具体而言，我们总结了通用网络流水线，重点梳理了信息融合技术，并探讨了有效的网络训练策略。我们在最主流的数据集上，对当前最优方法的占用感知性能进行了评估与分析。此外，本文还讨论了该领域面临的挑战与未来研究方向。我们希望本论文能为学界带来启发，推动更多3D占用感知相关研究的开展。本综述涵盖的完整研究列表已公开于一个持续收录最新成果的活跃仓库：https://github.com/HuaiyuanXu/3D-Occupancy-Perception。
英文摘要：
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.
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。
