Research on 3D Object Detection Method Based on Vlidar

Abstract

Aiming at the problem of insufficient accuracy of vision-based 3D object detection algorithms, this paper proposes a Vlidar-based 3D object detection network framework. In order to eliminate the difference between the two detection modes of lidar point cloud and image, it is proposed to convert the depth information of stereo vision into Vlidar representation. First, use the stereo disparity estimation algorithm to obtain the disparity map, convert the disparity map into a depth map after preprocessing, and then convert the depth map into Vlidar point cloud information. The experimental results show that compared with this advanced monocular 3D object detection algorithmic program smoke, the planned algorithmic program features 22 percent increase within the accuracy of 3D object detection among 30m, that is 27 percent completely different from the object detection algorithmic program supported lidar point cloud.