Progressive generation of 3D point clouds with hierarchical consistency

Peipei Li, Xiyan Liu, Jizhou Huang, Deguo Xia, Jianzhong Yang, Zhen Lu

Abstract: Highlights•We propose an effective generative model named hierarchical consistency variational autoencoder (HC-VAE) for the point cloud generation. Our model can generate dense point clouds of diverse shapes with uniformly distributed points in an unsupervised manner. We also hope that this research can be a stepping stone to more feasible hierarchical distributions-based generalization.•Hierarchical consistent mechanism (HCM) is presented to crisply constrain the model from both the shape distribution and the pointwise distribution, respectively. The hierarchical constraints seamlessly combine the learning of shape distribution and pointwise distribution in a complementary fashion, thereby facilitating the models perception of 3D shape and desensitization of the distribution of points given a shape.•Extensive qualitative and quantitative experiments demonstrate that HC-VAE achieves state-of-the-art performance in the 3D point cloud generation community with pleasing local topology details and distinguishable global shape information