ChartPointFlow for Topology-Aware 3D Point Cloud Generation
This work addresses the problem of accurately generating 3D point clouds with complex topological structures, such as holes and distinct subparts, which is a significant challenge for researchers and practitioners working with 3D data.
This paper introduces ChartPointFlow, a flow-based generative model for 3D point clouds that uses multiple latent labels to preserve topological structures. It achieves state-of-the-art performance in point cloud generation and reconstruction, and also demonstrates superior performance in unsupervised segmentation.
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 ChartPointFlow, 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 ChartPointFlow achieves state-of-the-art performance in terms of generation and reconstruction compared with other point cloud generators. Moreover, ChartPointFlow divides an object into semantic subparts using charts, and it demonstrates superior performance in case of unsupervised segmentation.