Ma Yuexin, Wang Tai, Bai Xuyang, Yang Huitong, Hou Yuenan, Wang Yaming, Qiao Yu, Yang Ruigang, Zhu Xinge
IEEE Trans Pattern Anal Mach Intell. 2024 Dec;46(12):10978-10997. doi: 10.1109/TPAMI.2024.3449912. Epub 2024 Nov 6.
In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. However, there has been no recent survey encompassing this novel and burgeoning research field. To catalyze future research, this paper presents a comprehensive survey of the latest developments in vision-centric BEV perception and its extensions. It compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms. Additionally, the paper provides in-depth analyses and comparative results on various BEV perception tasks, facilitating the evaluation of future works and sparking new research directions. Furthermore, the paper discusses and shares valuable empirical implementation details to aid in the advancement of related algorithms.
近年来，以视觉为中心的鸟瞰视图（BEV）感知因其固有的优势，如提供直观的世界表示并有利于数据融合，而受到了工业界和学术界的广泛关注。深度学习的快速发展催生了众多应对以视觉为中心的BEV感知挑战的方法。然而，最近还没有关于这个新颖且蓬勃发展的研究领域的综述。为了推动未来的研究，本文对以视觉为中心的BEV感知及其扩展的最新进展进行了全面综述。它汇编并整理了最新知识，对流行算法进行了系统回顾和总结。此外，本文还对各种BEV感知任务进行了深入分析和比较结果，便于对未来工作进行评估并激发新的研究方向。此外，本文还讨论并分享了有价值的经验性实现细节，以帮助推进相关算法。
