The Fusion Thoughts in 3D Object Detection for Autonomous Driving: A Survey
Zhen Li, Shi Zhou, Yuliang Gao, Yuting Wang, Yuren Du, Seiichi Serikawa, Lifeng Zhang · 2022
As the rapid development of computer vision, 3D object detection has become an indispensable method of perception for autonomous driving. LiDAR-only frameworks suffer from the sparsity of point clouds over the distance. While camera-only works suffer from the inaccurate depth measurement. Nevertheless, this paper proposed a LiDAR and camera fusion 3D detection framework, exploited and fused the spatiality of point clouds and the dense texture information of images together. Besides, we also utilized an automatic LiDAR and camera calibration method to ensure the accurate measurement while applied in embedded computation platform. Our experimental evaluation on KITTI dataset, the proposed work achieved good results on 3D detection accuracy and the automatic calibration method also made our work more stable and robust for different scenes.