3D Object Detection with Dynamic Weight Graph Convolution

3D object detection is one of the most critical technologies in autonomous driving,and 3D object detection based on LiDAR is usually carried out in the scene of point cloud construction.The current methods cannot fully use the point cloud’s structural information,leading to false and missed target detection.To solve this problem,we propose a DEG R-CNN based on dyna-mically weighted graph convolution.Firstly,the primary neighbour and subordinate neighbour are set for the node in RoI,and the graph structure of the point cloud is constructed.The geometric information of the object is restored.Then,Gaussian and 1D convolution are used in the graph to efficiently aggregate the point cloud’s structural features.Finally,the cross-attention mechanism adaptively fuses image features of different granularities to supplement the image semantic information.Experiments are conducted on KITTI dataset,and the effectiveness of modules is verified.The 3D mAP of the method reaches 88.80%,which is 1.22% higher than that of the baseline model.At the same time,the results of 3D object detection are visualized and analyzed in detail to understand performance and accuracy of the method better.