  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.
