Vision-Centric BEV Perception: A Survey

• URL: http://arxiv.org/abs/2208.02797v2
• Date: Wed, 7 Jun 2023 03:32:39 GMT
• Title: Vision-Centric BEV Perception: A Survey
• Authors: Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, Ruigang Yang, Dinesh Manocha, Xinge Zhu
• Abstract summary: Vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. This paper compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms.
• Score: 92.98068828762833
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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