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3D Shape Generation and Completion through Point-Voxel Diffusion

2021-04-08 10:38:03
Linqi Zhou, Yilun Du, Jiajun Wu

Abstract

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, our model, Point-Voxel Diffusion (PVD), is a unified, probabilistic formulation for unconditional shape generation and conditional, multi-modal shape completion. PVD marries denoising diffusion models with the hybrid, point-voxel representation of 3D shapes. It can be viewed as a series of denoising steps, reversing the diffusion process from observed point cloud data to Gaussian noise, and is trained by optimizing a variational lower bound to the (conditional) likelihood function. Experiments demonstrate that PVD is capable of synthesizing high-fidelity shapes, completing partial point clouds, and generating multiple completion results from single-view depth scans of real objects.

Abstract (translated)

URL

https://arxiv.org/abs/2104.03670

PDF

https://arxiv.org/pdf/2104.03670.pdf


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