Vision-Centric BEV Perception: A Survey
This survey addresses the lack of recent comprehensive overviews for researchers and practitioners in autonomous driving and robotics, though it is incremental as it organizes existing knowledge rather than proposing new methods.
This paper presents a comprehensive survey of vision-centric Bird's Eye View (BEV) perception, compiling up-to-date knowledge and providing systematic reviews, comparative results, and implementation details to catalyze future research in this burgeoning field.
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.