Abstract
3D object detection techniques rely on features obtained from point cloud data structures to identify and label the frame range of objects. Past techniques of converting point cloud data into voxel grids or image sets would make the data unnecessarily large, and it would be impractical to slice all space into a voxel grid of the same scale for objects of different types and depths. In this paper, the RGB-D depth camera is used to obtain the original point cloud information, and the mature 2D target detection technology and advanced 3D deep learning are used to locate the target. In addition, the voxel grid structure is improved, and the proportion and size of the voxel grid are appropriately adjusted by adopting the method of image category adaptation and spatial depth clustering to obtain more accurate point cloud features and achieve fast and accurate 3D object detection.
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