Vision-Centric BEV Perception: A Survey

TL;DR

This survey reviews recent progress in vision-centric Bird's Eye View perception, highlighting algorithms, results, and implementation insights to guide future research in this rapidly evolving field.

Contribution

It provides a comprehensive, systematic review and analysis of recent BEV perception methods, including comparative evaluations and practical implementation details.

Findings

Compilation of state-of-the-art algorithms

Analysis of performance across tasks

Insights into implementation practices

Abstract

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.