Non-uniform Point Cloud Upsampling via Local Manifold Distribution

Existing learning-based point cloud upsampling methods often overlook the intrinsic data distribution charac?teristics of point clouds, leading to suboptimal results when handling sparse and non-uniform point clouds. We propose a novel approach to point cloud upsampling by imposing constraints from the perspective of manifold distributions. Leveraging the strong fitting capability of Gaussian functions, our method employs a network to iteratively optimize Gaussian components and their weights, accurately representing local manifolds. By utilizing the probabilistic distribution properties of Gaussian functions, we construct a unified statistical manifold to impose distribution constraints on the point cloud. Experimental results on multiple datasets demonstrate that our method generates higher-quality and more uniformly distributed dense point clouds when processing sparse and non-uniform inputs, outperforming state-of-the-art point cloud upsampling techniques.

@article{fang2025_2504.11701,
  title={ Non-uniform Point Cloud Upsampling via Local Manifold Distribution },
  author={ Yaohui Fang and Xingce Wang },
  journal={arXiv preprint arXiv:2504.11701},
  year={ 2025 }
}