Chart Point Flow for Topology-Aware 3D Point Cloud Generation

A point cloud serves as a representation of the surface of a three dimensional (3D) shape. Deep generative models have been adapted to model their variations typically using a map from a ball-like set of latent variables. However, previous approaches did not pay much attention to the topological structure of a point cloud, despite that a continuous map cannot express the varying numbers of holes and intersections. Moreover, a point cloud is often composed of multiple subparts, and it is also difficult to express. In this study, we propose Chart Point Flow, a flow-based generative model with multiple latent labels for 3D point clouds. Each label is assigned to points in an unsupervised manner. Then, a map conditioned on a label is assigned to a continuous subset of a point cloud, similar to a chart of a manifold. This enables our proposed model to preserve the topological structure with clear boundaries, whereas previous approaches tend to generate blurry point clouds and fail to generate holes. The experimental results demonstrate that Chart Point Flow achieves state-of-the-art performance in terms of generation and reconstruction compared with other point cloud generators. Moreover, Chart Point Flow divides an object into semantic subparts using charts, and it demonstrates superior performance in case of unsupervised segmentation.