An improved point cloud completion method based on SnowflakeNet

Point cloud completion aims to complete partial point clouds captured from the real world, which is a crucial step in the pipeline of many point cloud tasks. Among the existing methods for solving this problem, SnowflakeNet is the most outstanding. However, SnowflakeNet cannot recover the detailed structure of point clouds in latent code because it uses many max-pooling operations in the encoding stage. Therefore, we propose an improved architecture to effectively acquire and preserve more detail information from input point clouds, thereby enhancing the quality of point cloud completion. Specifically, the improved lightweight DGCNN is added to the encoder to extract local features. The geometric perception block of PoinTr is introduced to extract the global features of the point cloud, which can fully model the structural information and inter-point relationships of known point clouds. The new optimizer Adan is also used in the training process to complete the partial point clouds. Comparative experiments on Completion3D and PCN datasets show that our method is better than most current point cloud completion methods.