Non-uniform Point Cloud Upsampling via Local Manifold Distribution
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913796272947200 |
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| author | Fang, Yaohui Wang, Xingce |
| author_facet | Fang, Yaohui Wang, Xingce |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_11701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-uniform Point Cloud Upsampling via Local Manifold Distribution Fang, Yaohui Wang, Xingce Computer Vision and Pattern Recognition Differential Geometry 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. |
| title | Non-uniform Point Cloud Upsampling via Local Manifold Distribution |
| topic | Computer Vision and Pattern Recognition Differential Geometry |
| url | https://arxiv.org/abs/2504.11701 |