论文标题
Pointinverter：通过具有形状先验的生成模型的点云重建和编辑
PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
论文作者
论文摘要
在本文中，我们提出了一种将3D点云映射到3D生成对抗网络的潜在空间的新方法。我们针对3D点云的生成模型基于SP-GAN，SP-GAN是最先进的球体引导的3D点云发生器。我们得出了一种有效的方法，将输入3D点云编码到SPGAN的潜在空间。我们的点云编码器可以在反转过程中解析点排序问题，因此可以确定生成的3D点云中的点与生成器使用的规范球中的点之间的对应关系。我们表明，我们的方法优于先前的3D点云的GAN反转方法，从定量和定性上实现最新的结果。我们的代码可在https://github.com/hkust-vgd/point_inverter上找到。
In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on SP-GAN, a state-of-the-art sphere-guided 3D point cloud generator. We derive an efficient way to encode an input 3D point cloud to the latent space of the SP-GAN. Our point cloud encoder can resolve the point ordering issue during inversion, and thus can determine the correspondences between points in the generated 3D point cloud and those in the canonical sphere used by the generator. We show that our method outperforms previous GAN inversion methods for 3D point clouds, achieving state-of-the-art results both quantitatively and qualitatively. Our code is available at https://github.com/hkust-vgd/point_inverter.
