Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint
This addresses quality issues in 3D shape generation for computer vision and graphics applications, though it appears incremental as it builds on existing diffusion frameworks.
The paper tackles the problem of non-smooth point surfaces in diffusion-based point cloud generation by incorporating a local smoothness constraint, resulting in realistic shapes and smoother point clouds that outperform state-of-the-art methods.
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.