  Diffusion models have been popular for point cloud generation tasks. Existing
works utilize the forward diffusion process to convert the original point
distribution into a noise distribution and then learn the reverse diffusion
process to recover the point distribution from the noise distribution. However,
the reverse diffusion process can produce samples with non-smooth points on the
surface because of the ignorance of the point cloud geometric properties. We
propose alleviating the problem by incorporating the local smoothness
constraint into the diffusion framework for point cloud generation. Experiments
demonstrate the proposed model can generate realistic shapes and smoother point
clouds, outperforming multiple state-of-the-art methods.
