Abstract: In the real scanning environment,due to the occlusion of line of sight or improper operation of technicians,the actual point cloud model has incomplete shape.The incompleteness of point cloud model has a serious impact on subsequent applications.Therefore,this paper proposes a 3D point cloud shape completion generative adversarial networks to complete the shape completion of point cloud model.The point cloud reconstruction part of the network combines the T-Net structure used for data alignment in PointNet with the 3D point cloud AutoEncoder network to complete the prediction and fill in the missing data.The discriminator uses the Encoder part of the 3D point cloud AutoEncoder to distinguish the completed 3D point cloud data from the real 3D point cloud data.Finally,in the ShapeNet trained the above network structure,the trained network model is verified and compared with other benchmark methods qualitatively.From the experimental results,it can be seen that the 3D point cloud shape completion generation adversarial network can complete the point cloud model with missing data into a complete 3D point cloud.In ShapeNet’s three sub-datasets chair,table,and bed,compared with the method based on 3D point cloud AutoEncoder,the F1 score is improved by 3.0%,3.3% and 3.1%,and compared with the method based on the voxel 3D-EPN method,the F1 score is increased by 9.9%,5.8%,and 4.3%,respectively.

Key words: 3D point cloud, AutoEncoder, Generative adversarial network, Shape completion