Non-uniform Point Cloud Upsampling via Local Manifold Distribution

• URL: http://arxiv.org/abs/2504.11701v1
• Date: Wed, 16 Apr 2025 01:54:33 GMT
• Title: Non-uniform Point Cloud Upsampling via Local Manifold Distribution
• Authors: Yaohui Fang, Xingce Wang,
• Abstract summary: We propose a novel approach to point cloud upsampling by imposing constraints from the perspective of manifold distributions.<n>We show that our method generates higher-quality and more uniformly distributed dense point clouds when processing sparse and non-uniform inputs.
• Score: 3.882709754150705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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