A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors
Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplificati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7491 |
| _version_ | 1797476868718329856 |
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| author | Zhiyuan Shi Weiming Xu Hao Meng |
| author_facet | Zhiyuan Shi Weiming Xu Hao Meng |
| author_sort | Zhiyuan Shi |
| collection | DOAJ |
| description | Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly. |
| first_indexed | 2024-03-09T21:09:52Z |
| format | Article |
| id | doaj.art-216ea431ee9949728ad47c89a2aad04b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:09:52Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-216ea431ee9949728ad47c89a2aad04b2023-11-23T21:50:05ZengMDPI AGSensors1424-82202022-10-012219749110.3390/s22197491A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning SensorsZhiyuan Shi0Weiming Xu1Hao Meng2Department of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaConventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.https://www.mdpi.com/1424-8220/22/19/74913D scanning sensorspoint cloud simplificationfeature indexbounding boxkd-treeanalytic hierarchy process |
| spellingShingle | Zhiyuan Shi Weiming Xu Hao Meng A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors Sensors 3D scanning sensors point cloud simplification feature index bounding box kd-tree analytic hierarchy process |
| title | A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors |
| title_full | A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors |
| title_fullStr | A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors |
| title_full_unstemmed | A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors |
| title_short | A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors |
| title_sort | point cloud simplification algorithm based on weighted feature indexes for 3d scanning sensors |
| topic | 3D scanning sensors point cloud simplification feature index bounding box kd-tree analytic hierarchy process |
| url | https://www.mdpi.com/1424-8220/22/19/7491 |
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