远距离和遮挡下三维目标检测算法研究OA北大核心CSTPCD

To address the limitations of existing 3D target detection algorithms,particularly their poor detection per-formance for occluded and long-distance objects,we have implemented an enhancement to the PointRCNN network,a 3D object detection algorithm based on point cloud.We began by voxelizing the region of interest obtained from the re-gion proposal network and constructing region pyramids of different scales to capture a wider range of points of interest.Simultaneously,we introduced a point cloud transformer module to enhance the learning of the local features of grid center points.Moreover,we incorporated a sphere query radius prediction module into the network.This addition al-lows the model to adaptively adjust the sphere query range according to the density of the point cloud.Finally,the ef-fectiveness of the proposed algorithm was validated through rigorous experimental testing.We evaluated the perform-ance of the model using the KITTI data set and designed corresponding ablation experiments to verify the effectiveness of each module in the model.