We present a probabilistic model for point cloud generation, which is
fundamental for various 3D vision tasks such as shape completion, upsampling,
synthesis and data augmentation. Inspired by the diffusion process in
non-equilibrium thermodynamics, we view points in point clouds as particles in
a thermodynamic system in contact with a heat bath, which diffuse from the
original distribution to a noise distribution. Point cloud generation thus
amounts to learning the reverse diffusion process that transforms the noise
distribution to the distribution of a desired shape. Specifically, we propose
to model the reverse diffusion process for point clouds as a Markov chain
conditioned on certain shape latent. We derive the variational bound in closed
form for training and provide implementations of the model. Experimental
results demonstrate that our model achieves competitive performance in point
cloud generation and auto-encoding. The code is available at
https://github.com/luost26/diffusion-point-cloud.

本文提出了一个概率模型，将点云看作是处于非平衡热力学系统内的粒子，并通过扩散过程学习将噪声分布转换为所需形状分布的逆扩散过程，即点云生成过程。具体地，将点云的逆扩散过程建模为一个以某个形状因子为条件的马尔科夫链，并提供了闭合形式的变分下界以进行训练。实验结果显示，该模型在点云生成和自编码方面具有竞争性能。