MF-Net：Multi-Scale Feature Point Cloud Completion Network Combined with Residual Network

Aiming at the problem of semantic information loss caused by the current point cloud completion network focusing only on global features, a point cloud completion network based on multi-scale feature extraction of residual network is proposed. The network adopts an end-to-end idea. Firstly, to avoid the problem of incomplete single feature, the original input is sampled into three different scale point clouds. Then, the global features of the low-resolution point cloud extracted by different methods and the local features of the original point cloud are fused recursively in a cascade way to form feature vectors, and are put into the fully connected network to realize the prediction of coarse point cloud. Next, the splicing original point cloud and coarse point cloud are sent into the fine reconstruction unit, and then the attention mechanism is integrated into the fine reconstruction unit, and the residual network is used to complete from rough to fine. Finally, the joint loss function among coarse point clouds, dense point clouds and ground truth point clouds is calculated to improve the completion performance. Experiments on ShapeNet and KITTI data sets show that the proposed method has good completion effect on the incomplete point cloud, both in qualitative and quantitative comparison, and also show its generalization ability. © The Author(s) 2023.