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3D Object Detection for Autonomous Driving: A Comprehensive Survey

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Abstract

Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers’ burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which aims to predict the locations, sizes, and categories of the 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area.

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Acknowledgements

This project is funded in part by the National Key R &D Program of China Project 2022ZD0161100, by the Centre for Perceptual and Interactive Intelligence (CPIl) Ltd under the Innovation and Technology Commission (ITC)’s InnoHK, by General Research Fund Project 14204021 and Research Impact Fund Project R5001-18 of Hong Kong RGC. Hongsheng Li and Xiaogang Wang are PIs of CPII under the InnoHK.

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  1. The Chinese University of Hong Kong, Hong Kong, China

    Jiageng Mao, Xiaogang Wang & Hongsheng Li

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Shaoshuai Shi

  3. Centre for Perceptual and Interactive Intelligence, Hong Kong, China

    Xiaogang Wang & Hongsheng Li

  4. Shanghai AI Laboratory, Shanghai, China

    Hongsheng Li

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Mao, J., Shi, S., Wang, X. et al. 3D Object Detection for Autonomous Driving: A Comprehensive Survey. Int J Comput Vis 131, 1909–1963 (2023). https://doi.org/10.1007/s11263-023-01790-1

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