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.

翻译：近年来，以视觉为中心的鸟瞰图（BEV）感知因其固有优势（如提供直观的世界表征及有利于数据融合）而引起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大量应对以视觉为中心的BEV感知挑战的方法。然而，针对这一新兴且蓬勃发展的研究领域，近期尚未有综述进行系统梳理。为促进未来研究，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。本文汇编并整理了最新知识，对主流算法进行了系统回顾与总结。此外，本文对多项BEV感知任务提供了深入分析与对比结果，这有助于评估未来工作并激发新的研究方向。最后，本文讨论并分享了宝贵的工程实现细节，以推动相关算法的进步。