OTD: Diffusion on OT-Structured Point Clouds for 3d Shape Generation

Recently, diffusion models have shown strong potential for 3D point cloud generation, but capturing point distributions remains difficult due to data sparsity and irregularity. We propose a method that integrates Optimal Transport with diffusion models. Specifically, point clouds are first mapped into a voxel grid via Optimal Transport, then processed with diffusion’s forward and reverse stages. To improve the quality of generated point clouds, we design a point cloud–specific image-level loss and adopt a weighted objective with an attention denoising network. Experiments on ShapeNet demonstrate that our approach generates high-quality, diverse point clouds with improved efficiency compared to existing diffusion-based methods.