计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6): 214-231.doi: 10.11896/jsjkx.250400111

• 计算机图形学&多媒体 • 上一篇    下一篇

基于激光雷达与相机融合的3D目标检测综述

纪文宇1, 李阳1, 王家宝1, 付睿智2, 刘小语1, 苗壮1   

  1. 1 陆军工程大学指挥控制工程学院 南京 210007
    2 中国人民解放军32316部队 乌鲁木齐 830000
  • 收稿日期:2025-04-23 修回日期:2025-07-14 出版日期:2026-06-15 发布日期:2026-06-09
  • 通讯作者: 苗壮(emiao_beyond@163.com)
  • 作者简介:(wenyuji_edu@foxmail.com)
  • 基金资助:
    国家自然科学基金(62273356);高层次人才创新工程(KYZYJQJY2101);国家人才项目(2022-JCJQ-ZQ-001);江苏省重点研发计划(BE2023809)

Review of 3D Object Detection Based on LiDAR-camera Fusion

JI Wenyu1, LI Yang1, WANG Jiabao1, FU Ruizhi2, LIU Xiaoyu1, MIAO Zhuang1   

  1. 1 College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 Unit 32316 of PLA,Urumqi 830000,China
  • Received:2025-04-23 Revised:2025-07-14 Published:2026-06-15 Online:2026-06-09
  • About author:JI Wenyu,born in 2002,postgraduate.His main research interests include deep learning and computer vision.
    MIAO Zhuang,born in 1976,Ph.D,professor,is a member of CCF(No.E20031151).His main research in-terests include artificial intelligence,pattern recognition and computer vision.
  • Supported by:
    National Natural Science Foundation of China(62273356),High-level Talents Innovation Project(KYZYJQJY2101),National Talent Project of China(2022-JCJQ-ZQ-001) and Provincial Primary Research & Development Plan of Jiangsu (BE2023809).

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摘要： 多模态融合的3D目标检测是自动驾驶、人机协作等领域的一项基本任务,近年来受到广泛关注。通过融合激光雷达和相机数据,3D目标检测可以有效传递信息并整合特征,进而提升对复杂环境的理解和目标检测的精度。然而,随着融合方法的不断增多,适用于稠密场景的传统融合方法面临着计算成本高、检测范围受限等问题,难以满足远距离检测的现实需求。因此,一些方法开始探索新的融合架构,旨在突破稀疏场景下的检测瓶颈。作为3D融合目标检测领域按照稠密与稀疏场景全新视角进行分类的首篇中文综述,全面分析了现有文献综述的特点,总结回顾了近年来该领域的发展变化。主要贡献如下:1)根据是否引入稠密的鸟瞰视角特征图,提出了包含稠密级融合与稀疏级融合两大类以及五小类的分类方法,指出了不同融合策略的特点及其应用场景;2)详细梳理了现有15个公开数据集,针对3个主流数据集,介绍了评价指标并对比了10余种不同方法在主流数据集上的实验结果;3)深入探究了典型融合方法的局限性,并展望了该领域的发展趋势,指出了未来可能的研究方向。

关键词: 激光雷达, 相机图像, 点云, 3D目标检测, 传感器融合

Abstract: Multi-modal 3D object detection,as a fundamental task in autonomous driving and human-robot collaboration,has garnered significant attention in recent years.By integrating LiDAR and camera data,3D object detection facilitates effective information transmission and feature consolidation,thereby enhancing the understanding of complex environments and improving detection accuracy.However,as the number of fusion methodologies increases,traditional methods designed for dense scenarios encounter problems such as high computational costs and limited detection ranges,making them insufficient to meet the real-world requirements for long-range detection.Consequently,emerging methods are increasingly focused on developing novel fusion architectures to address detection challenges in sparse scenarios.This paper represents the first Chinese-language review to classify multi-modal 3D object detection methods from the novel perspective of dense scenarios versus sparse scenarios,providing a comprehensive analysis of the characteristics of existing literature and summarizing the evolutionary trends in this field.The primary contributions of this work are as follows:1)A novel classification framework is proposed,distinguishing between dense-level and sparse-level fusion based on the incorporation of dense Bird's-Eye-View feature maps,encompassing two major categories and five subcategories,while elucidating the characteristics and application scenarios of different fusion strategies;2)A detailed review of 15 publicly available datasets is conducted,with a focus on evaluation metrics for 3 mainstream datasets,and a comparative analysis of experimental results from over 10 distinct methods on these benchmarks is provided;3)The limitations of representative fusion methods are critically examined,and future research directions are outlined,offering insights into potential advancements in the field.

Key words: LiDAR, Camera images, Point cloud, 3D object detection, Sensor fusion

中图分类号: 

  • TP391
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