Lightweight Vehicle Point Cloud Completion Network Combined with Channel Pruning and Channel Attention
A lightweight point cloud completion network is proposed to accurately and efficiently repair incomplete vehicle point clouds in autonomous driving scenarios,addressing the efficiency issue often overlooked by existing point cloud completion networks that primarily focus on accuracy.Firstly,to enhance inference efficiency,an efficient one-shot channel pruning technique is employed to improve the completion speed of the network.Secondly,a channel attention module is integrated into the network during the feature extraction phase,which combines weighted features with global features.This dual-layered multidimensional feature extraction yields the final feature vector.Subsequently,the feature vector is fed into a dual decoder structure,generating dense coarse point clouds and input point cloud deviation values through fully connected layers and multi-layer perceptron,respectively.Finally,by adding the coarse point cloud and input point cloud deviation values,a refined and complete point cloud is obtained.Experimental evaluations conducted on the PCN dataset and KITTI dataset demonstrate significant improvements in real-time completion of missing vehicle information.Moreover,favorable results are achieved in terms of completion accuracy as well.
point cloud completionchannel pruningchannel attentionlightweightdeep learning
万方数据
维普
