WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation

Yingzhi Tang, Yue Qian, Qijian Zhang, Yiming Zeng, Junhui Hou, Xuefei Zhe

Abstract: We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also in-geniously utilize the principle of the discriminator and tai-lor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel gen-erating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently gen-erating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods in terms of quantitative metrics, visual quality, and efficiency. The source code is publicly available at https://github.com/yztang4/WarpingGAN.git.