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.

本文提出了一种新颖的概率生成建模方法，称为 Point-Voxel Diffusion（PVD），它是一个统一的、概率的公式，用于无条件的形状生成和多模态形状完成。PVD 将降噪扩散模型与 3D 形状的混合点 - 体素表示相结合，并通过优化变分下界来训练（条件）似然函数。实验证明了 PVD 的高保真度合成能力，以及从单视角深度扫描的真实对象中生成多个完成结果的能力。