Point cloud denoising using a generalized error metric
Effective removal of noises from raw point clouds while preserving geometric features is the key challenge for point cloud denoising. To address this problem, we propose a novel method that jointly optimizes the point positions and normals. To preserve geometric features, our formulation uses a gene...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Graphical Models |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1524070324000043 |
| _version_ | 1797256415123865600 |
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| author | Qun-Ce Xu Yong-Liang Yang Bailin Deng |
| author_facet | Qun-Ce Xu Yong-Liang Yang Bailin Deng |
| author_sort | Qun-Ce Xu |
| collection | DOAJ |
| description | Effective removal of noises from raw point clouds while preserving geometric features is the key challenge for point cloud denoising. To address this problem, we propose a novel method that jointly optimizes the point positions and normals. To preserve geometric features, our formulation uses a generalized robust error metric to enforce piecewise smoothness of the normal vector field as well as consistency between point positions and normals. By varying the parameter of the error metric, we gradually increase its non-convexity to guide the optimization towards a desirable solution. By combining alternating minimization with a majorization-minimization strategy, we develop a numerical solver for the optimization which guarantees convergence. The effectiveness of our method is demonstrated by extensive comparisons with previous works. |
| first_indexed | 2024-04-24T22:21:23Z |
| format | Article |
| id | doaj.art-66f78af318f44a1ea81ae2adc867e4d8 |
| institution | Directory Open Access Journal |
| issn | 1524-0703 |
| language | English |
| last_indexed | 2024-04-24T22:21:23Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Graphical Models |
| spelling | doaj.art-66f78af318f44a1ea81ae2adc867e4d82024-03-20T06:08:49ZengElsevierGraphical Models1524-07032024-06-01133101216Point cloud denoising using a generalized error metricQun-Ce Xu0Yong-Liang Yang1Bailin Deng2Tsinghua University, ChinaUniversity of Bath, UKCardiff University, UK; Corresponding author.Effective removal of noises from raw point clouds while preserving geometric features is the key challenge for point cloud denoising. To address this problem, we propose a novel method that jointly optimizes the point positions and normals. To preserve geometric features, our formulation uses a generalized robust error metric to enforce piecewise smoothness of the normal vector field as well as consistency between point positions and normals. By varying the parameter of the error metric, we gradually increase its non-convexity to guide the optimization towards a desirable solution. By combining alternating minimization with a majorization-minimization strategy, we develop a numerical solver for the optimization which guarantees convergence. The effectiveness of our method is demonstrated by extensive comparisons with previous works.http://www.sciencedirect.com/science/article/pii/S1524070324000043Geometry processingOptimizationPoint cloud denoising |
| spellingShingle | Qun-Ce Xu Yong-Liang Yang Bailin Deng Point cloud denoising using a generalized error metric Graphical Models Geometry processing Optimization Point cloud denoising |
| title | Point cloud denoising using a generalized error metric |
| title_full | Point cloud denoising using a generalized error metric |
| title_fullStr | Point cloud denoising using a generalized error metric |
| title_full_unstemmed | Point cloud denoising using a generalized error metric |
| title_short | Point cloud denoising using a generalized error metric |
| title_sort | point cloud denoising using a generalized error metric |
| topic | Geometry processing Optimization Point cloud denoising |
| url | http://www.sciencedirect.com/science/article/pii/S1524070324000043 |
| work_keys_str_mv | AT quncexu pointclouddenoisingusingageneralizederrormetric AT yongliangyang pointclouddenoisingusingageneralizederrormetric AT bailindeng pointclouddenoisingusingageneralizederrormetric |