3D Object Detection method based on point Density perception
Most of the current 3D object detection methods use the farthest point sampling for feature extraction,but ignore the effect of point cloud density information.In order to further enhance the ability of sparse convolution and improve the amount of target feature information,proposes a dynamic sparse convolution point density perception network(DS-PDP).Firstly,the point cloud is voxelized by dynamic sparse convolution down-sampling,and the voxel feature information is located from the dynamic sparse volume by voxel point centroid localization.The grid pool of the region of interest is used to multi-scale aggregate the point centroid density fea-tures,and finally the target confidence is predicted,and competitive results are achieved on the KITT I dataset.
Dynamic sparse convolutionVoxel point centroidPoint densityRoI
NETL
NSTL
万方数据
