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Vision-Centric BEV Perception: A Survey

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arxiv 2208.02797 v2 pith:FDT73ZVO submitted 2022-08-04 cs.CV

Yuexin Ma , Tai Wang , Xuyang Bai , Huitong Yang , Yuenan Hou , Yaming Wang , Yu Qiao , Ruigang Yang
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Dinesh Manocha Xinge Zhu
This is my paper · ORCID
classification cs.CV
keywords perceptionvision-centricresearchsurveyalgorithmsfuturerecentacademia
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VADER: Adaptive Debiasing for Hallucination Mitigation in Video Large Language Models

    cs.CV 2026-08 conditional novelty 7.0 of 10

    A training-free decoding framework that adaptively reweights attention toward video tokens and erases key visual evidence per frame to suppress hallucinated predictions, achieving 72.60% accuracy on EventHallusion wit...

  2. A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection

    cs.CV 2024-12 conditional novelty 6.0 of 10

    An adversarial semantic perturbation search, SimpleDIRECT, exposes larger worst-case robustness gaps in BEV detection models than random natural corruptions, and ranks ten models on nuScenes.

  3. TopView: Vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning

    cs.CV 2024-12 reject novelty 4.0 of 10

    TopView predicts a vanishing point with a neural network and builds a homography that maps detected road users into a vectorized bird's eye view without camera calibration.

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