Multi-modality 3D object detection in autonomous driving: A review

Autonomous driving perception has made significant strides in recent years, but accurately sensing the environment using a single sensor remains a daunting task. This review offers a comprehensive overview of the current research on LiDAR and camera fusion for 3D object detection in multi-modality domains. The review first identifies the perception task, open public detection dataset, and data representation related to 3D object detection. It then presents an in-depth survey of coarse-grained and fine-grained fusion approaches, reporting their respective performances on the KITTI and nuScenes datasets. The review identifies general trends in multi-modality 3D object detection and provides insights and promising research directions based on these observations. Additionally, the review summarizes the current challenges of fusion strategies for perception problems in autonomous driving. Based on a critical review of existing literature, this paper identifies and discusses key research directions in the field of fusion-based 3D object detection approach for perception problems in autonomous driving, which is instructive for future work.