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 ingeniously utilize the principle of the discriminator and tailor 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 generating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently generating 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.
翻译:我们建议WarpingGAN, 一个高效高效的3D点云生成网络。 与直接学习潜在代码和3D形状之间映射功能而生成点云的现有方法不同, Werping-GAN学习一个统一的本地扭曲功能,将多个相同的预定义前科(即固定在正常的3D网格上分布的一组点)转换成3D形状。 此外,我们还巧妙地利用歧视者的原则,缝合损失,以消除与不同前科相对应的生成形状不同分区之间的差距,以提高质量。 由于新颖的生成机制,WarpingGAN(一次性培训后的一个单一的轻量级网络)能够以不同分辨率有效生成统一分布的3D点云。 广泛的实验结果表明我们的WerpingGAN在定量测量、视觉质量和效率方面优于国家艺术方法。 源代码公布于https://github.com/yztang4/SwarpingGAN.git。