  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.
