Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects
Laser scanners are widely used to collect coordinates, also known as point-clouds, of three-dimensional free-form objects. For creating a solid model from a given point-cloud and transferring the data from the model, features-based optimization of the point-cloud to minimize the number if points in...
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MDPI AG
2018-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/7/2239 |
| _version_ | 1811304131150741504 |
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| author | Yufu Zang Bisheng Yang Fuxun Liang Xiongwu Xiao |
| author_facet | Yufu Zang Bisheng Yang Fuxun Liang Xiongwu Xiao |
| author_sort | Yufu Zang |
| collection | DOAJ |
| description | Laser scanners are widely used to collect coordinates, also known as point-clouds, of three-dimensional free-form objects. For creating a solid model from a given point-cloud and transferring the data from the model, features-based optimization of the point-cloud to minimize the number if points in the cloud is required. To solve this problem, existing methods mainly extract significant points based on local surface variation of a predefined level. However, comprehensively describing an object’s geometric information using a predefined level is difficult since an object usually has multiple levels of details. Therefore, we propose a simplification method based on a multi-level strategy that adaptively determines the optimal level of points. For each level, significant points are extracted from the point cloud based on point importance measured by both local surface variation and the distribution of neighboring significant points. Furthermore, the degradation of perceptual quality for each level is evaluated by the adjusted mesh structural distortion measurement to select the optimal level. Experiments are performed to evaluate the effectiveness and applicability of the proposed method, demonstrating a reliable solution to optimize the adaptive laser scanning of point clouds for free-forms objects. |
| first_indexed | 2024-04-13T08:00:34Z |
| format | Article |
| id | doaj.art-cf72dc554e53433ba0b129c0890aeefa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:00:34Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cf72dc554e53433ba0b129c0890aeefa2022-12-22T02:55:17ZengMDPI AGSensors1424-82202018-07-01187223910.3390/s18072239s18072239Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form ObjectsYufu Zang0Bisheng Yang1Fuxun Liang2Xiongwu Xiao3School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaLaser scanners are widely used to collect coordinates, also known as point-clouds, of three-dimensional free-form objects. For creating a solid model from a given point-cloud and transferring the data from the model, features-based optimization of the point-cloud to minimize the number if points in the cloud is required. To solve this problem, existing methods mainly extract significant points based on local surface variation of a predefined level. However, comprehensively describing an object’s geometric information using a predefined level is difficult since an object usually has multiple levels of details. Therefore, we propose a simplification method based on a multi-level strategy that adaptively determines the optimal level of points. For each level, significant points are extracted from the point cloud based on point importance measured by both local surface variation and the distribution of neighboring significant points. Furthermore, the degradation of perceptual quality for each level is evaluated by the adjusted mesh structural distortion measurement to select the optimal level. Experiments are performed to evaluate the effectiveness and applicability of the proposed method, demonstrating a reliable solution to optimize the adaptive laser scanning of point clouds for free-forms objects.http://www.mdpi.com/1424-8220/18/7/2239adaptive representationgeometric multi-levelsurface variationradial basis functionperceptual quality |
| spellingShingle | Yufu Zang Bisheng Yang Fuxun Liang Xiongwu Xiao Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects Sensors adaptive representation geometric multi-level surface variation radial basis function perceptual quality |
| title | Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects |
| title_full | Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects |
| title_fullStr | Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects |
| title_full_unstemmed | Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects |
| title_short | Novel Adaptive Laser Scanning Method for Point Clouds of Free-Form Objects |
| title_sort | novel adaptive laser scanning method for point clouds of free form objects |
| topic | adaptive representation geometric multi-level surface variation radial basis function perceptual quality |
| url | http://www.mdpi.com/1424-8220/18/7/2239 |
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