Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization
The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting featur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-07-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/7/1073 |
| _version_ | 1818958376757886976 |
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| author | Yuan Wang Jiajing Wang Xiuwan Chen Tianxing Chu Maolin Liu Ting Yang |
| author_facet | Yuan Wang Jiajing Wang Xiuwan Chen Tianxing Chu Maolin Liu Ting Yang |
| author_sort | Yuan Wang |
| collection | DOAJ |
| description | The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial components. The method analyzes the normal vector distribution matrix to segment feature points from a 3D point cloud. The point cloud is then divided into different patches by applying the region growing method on the basis of the distance constraint and according to the initial results. Subsequently, each patch is fitted with an implicit expression equation, and the proposed method is combined with the random sample consensus (RANSAC) algorithm and parameter fitting to extract and optimize the feature surface. The proposed method is experimentally validated on three industrial components. The threshold setting in the algorithm is discussed in terms of algorithm principles and model features. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components. |
| first_indexed | 2024-12-20T11:24:46Z |
| format | Article |
| id | doaj.art-2c8eb9465ece4d3a858feed1f8f1a308 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:24:46Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2c8eb9465ece4d3a858feed1f8f1a3082022-12-21T19:42:24ZengMDPI AGRemote Sensing2072-42922018-07-01107107310.3390/rs10071073rs10071073Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and OptimizationYuan Wang0Jiajing Wang1Xiuwan Chen2Tianxing Chu3Maolin Liu4Ting Yang5Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, ChinaTencent Technology (Beijing) Company Limited, China Technology Trade Center, No.66 North 4th Ring West Road, Hai Dian District, Beijing 100080, ChinaInstitute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, ChinaConrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USAInstitute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, ChinaInstitute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, ChinaThe structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial components. The method analyzes the normal vector distribution matrix to segment feature points from a 3D point cloud. The point cloud is then divided into different patches by applying the region growing method on the basis of the distance constraint and according to the initial results. Subsequently, each patch is fitted with an implicit expression equation, and the proposed method is combined with the random sample consensus (RANSAC) algorithm and parameter fitting to extract and optimize the feature surface. The proposed method is experimentally validated on three industrial components. The threshold setting in the algorithm is discussed in terms of algorithm principles and model features. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components.http://www.mdpi.com/2072-4292/10/7/10733D point cloudfeature surface extractionRANSACregion growingsegmentation and optimizationindustrial components |
| spellingShingle | Yuan Wang Jiajing Wang Xiuwan Chen Tianxing Chu Maolin Liu Ting Yang Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization Remote Sensing 3D point cloud feature surface extraction RANSAC region growing segmentation and optimization industrial components |
| title | Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization |
| title_full | Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization |
| title_fullStr | Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization |
| title_full_unstemmed | Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization |
| title_short | Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization |
| title_sort | feature surface extraction and reconstruction from industrial components using multistep segmentation and optimization |
| topic | 3D point cloud feature surface extraction RANSAC region growing segmentation and optimization industrial components |
| url | http://www.mdpi.com/2072-4292/10/7/1073 |
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