Training PointNet for human point cloud segmentation with 3D meshes

Abstract

PointNet, which enables end-to-end learning for scattered/unordered point data, is a popular neural network architecture. However, in many applications, large amounts of complete point clouds are hardly available for non-rigid objects such as the human body. To generate the training data of PointNet, in this study, we propose to generate human body point clouds of various postures by uniformly sampling point clouds from meshes with respect to multiple human mesh model datasets. Experiments show that the model trained with the point clouds generated from mesh data is effective in the task of human body segmentation.