A study of colored point cloud completion for a human head

Abstract

A depth image of a single RGBD camera has many occlusions and noises, so it is not easy to obtain 3D data of the whole human head. Point cloud deep learning has recently attracted much attention, which allows direct input and output of point clouds. One of them, the point cloud completion, which creates a complete point cloud from a partial point cloud, has been studied. However, existing studies of point cloud completion evaluated only the shape and have not focused on colored point clouds. Therefore, this study proposes a colored point cloud completion method for the human head based on machine learning. For deep learning training, the CG dataset was created from the face and hair dataset. The proposed network inputs and outputs point cloud with XYZ coordinates, and 𝐿 ∗𝑎 ∗𝑏 ∗ color information optionally has a Discriminator that processes 𝐿 ∗𝑎 ∗𝑏 ∗ -D images by a differentiable point renderer. This study experimented using the network and the dataset and evaluated using point domain and image domain metrics.