PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
This addresses the challenge of 3D point cloud inversion and editing for computer vision and graphics applications, but it is incremental as it builds on existing SP-GAN methods.
The paper tackles the problem of mapping 3D point clouds to the latent space of a generative adversarial network (SP-GAN) for reconstruction and editing, achieving state-of-the-art results quantitatively and qualitatively.
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.