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