  We present a probabilistic model for point cloud generation, which is
critical 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 the state-of-the-art performance in
point cloud generation and auto-encoding. The code is available at
\url{https://github.com/luost26/diffusion-point-cloud}.
