Non-uniform Point Cloud Upsampling via Local Manifold Distribution

Yaohui Fang; Xingce Wang

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arXiv · 2504.11701

Non-uniform Point Cloud Upsampling via Local Manifold Distribution

Yaohui Fang·Xingce Wang

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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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Yaohui Fang, Xingce Wang. 2025-04-16. Non-uniform Point Cloud Upsampling via Local Manifold Distribution. https://arxiv.org/abs/2504.11701

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