Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds
Parametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and split-connect algorithms, still suffer the proble...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/24/9816 |
| _version_ | 1797379388742828032 |
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| author | Jian Wang Sheng Bi Wenkang Liu Liping Zhou Tukun Li Iain Macleod Richard Leach |
| author_facet | Jian Wang Sheng Bi Wenkang Liu Liping Zhou Tukun Li Iain Macleod Richard Leach |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | Parametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and split-connect algorithms, still suffer the problems of low computational efficiency, especially in the case of large data scales and high accuracy requirements. This paper proposes a speed-improved algorithm for fast, large-scale freeform point cloud fitting by stitching locally fitted T-splines through three steps of localized operations. Experiments show that the proposed algorithm produces a three-to-eightfold efficiency improvement from the global and local fitting algorithms, and a two-to-fourfold improvement from the latest split-connect algorithm, in high-accuracy and large-scale fitting scenarios. A classical Lena image study showed that the algorithm is at least twice as fast as the split-connect algorithm using fewer than 80% control points of the latter. |
| first_indexed | 2024-03-08T20:23:31Z |
| format | Article |
| id | doaj.art-de01929206634a94adae7d1bf70e179a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:23:31Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-de01929206634a94adae7d1bf70e179a2023-12-22T14:40:51ZengMDPI AGSensors1424-82202023-12-012324981610.3390/s23249816Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point CloudsJian Wang0Sheng Bi1Wenkang Liu2Liping Zhou3Tukun Li4Iain Macleod5Richard Leach6State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaCentre for Precision Technologies, University of Huddersfield, Huddersfield HD1 3DH, UKIMA Ltd., 29 Clay Lane, Hale, Cheshire WA15 8PJ, UKFaculty of Engineering, University of Nottingham, Nottingham NG8 1BB, UKParametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and split-connect algorithms, still suffer the problems of low computational efficiency, especially in the case of large data scales and high accuracy requirements. This paper proposes a speed-improved algorithm for fast, large-scale freeform point cloud fitting by stitching locally fitted T-splines through three steps of localized operations. Experiments show that the proposed algorithm produces a three-to-eightfold efficiency improvement from the global and local fitting algorithms, and a two-to-fourfold improvement from the latest split-connect algorithm, in high-accuracy and large-scale fitting scenarios. A classical Lena image study showed that the algorithm is at least twice as fast as the split-connect algorithm using fewer than 80% control points of the latter.https://www.mdpi.com/1424-8220/23/24/9816T-splinefreeform fittinglarge-scale point cloudstitching splines |
| spellingShingle | Jian Wang Sheng Bi Wenkang Liu Liping Zhou Tukun Li Iain Macleod Richard Leach Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds Sensors T-spline freeform fitting large-scale point cloud stitching splines |
| title | Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds |
| title_full | Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds |
| title_fullStr | Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds |
| title_full_unstemmed | Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds |
| title_short | Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds |
| title_sort | stitching locally fitted t splines for fast fitting of large scale freeform point clouds |
| topic | T-spline freeform fitting large-scale point cloud stitching splines |
| url | https://www.mdpi.com/1424-8220/23/24/9816 |
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