Human Body Point Clouds Location and Color Complement Based on Point Clouds Migration

‍ ‍The point clouds obtained by 3D scanning equipment have problems， such as target occlusion， capture perspective， etc.， resulting in the lack of point clouds. Point cloud completion is a significant work for downstream tasks. However， the existing point cloud completion work focuses on the completion of simple objects， and there is a lack of research on point cloud completion of complex human body. In order to introduce the point cloud completion task into the downstream human reconstruction， this paper proposed a coarse to fine human point cloud prediction method to reconstruction human point cloud position and color. The method in this paper created a human point cloud dataset with fine clothing texture， adopted the network structure of point cloud migration， made full use of the global rough information of point cloud， and combined multi-layer network to predict the complete point cloud， so as to optimize the up-sampling scheme； Then， the color was completed on the point cloud after up-sampling， and the eigenvalues were diffused and extracted through part of the point cloud color information； Finally， a multi-layer perceptron was used for color prediction. The experimental results on human dataset shows that this method not only has better performance in objective indicators， but also can ensure a more complete and clear edge point cloud in subjective quality compared with the state-of-the-art methods.