Deformable cylinder extraction from LiDAR point cloud using candidate selection
Cylinder extraction is a fundamental task in point cloud-based environmental mapping such as tree modeling and reverse engineering. However, current methods are hindered by data missing, and their performance on deformable cylinders remains to be improved. To address these challenges, this paper pro...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2502612 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224326488784896 |
|---|---|
| author | Meng Du Di Cao Cheng Wang Sheng Nie Jingru Wang |
| author_facet | Meng Du Di Cao Cheng Wang Sheng Nie Jingru Wang |
| author_sort | Meng Du |
| collection | DOAJ |
| description | Cylinder extraction is a fundamental task in point cloud-based environmental mapping such as tree modeling and reverse engineering. However, current methods are hindered by data missing, and their performance on deformable cylinders remains to be improved. To address these challenges, this paper proposes an unsupervised approach for robustly extracting deformable cylinders using a candidate selection strategy. A deformable cylinder is represented by a skeleton and cross-sectional circles. First, preprocessing is performed to downsample the raw point cloud and estimate normals. Second, cylinder extraction is conducted to obtain the initial cylinder segments. Then, candidate selection is applied to evolve the initial cylinder segments, extending the skeletons and generating cross-sectional circles to form the initial deformable cylinders. Finally, a refinement step is performed to optimize these cylinders. Experimental results validate the overall effectiveness and robustness of the algorithm against data missing. The average precision, recall, and F1 score are 0.95, 0.90, and 0.92, respectively, in the point cloud collected from four real-world pipeline scenarios. This algorithm supports practical applications such as clearance space measurement and Building Information Modeling (BIM) generation for pipeline systems. |
| format | Article |
| id | doaj-art-d5c3c02df57c4ba3ab32b68b341794e3 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-d5c3c02df57c4ba3ab32b68b341794e32025-08-25T11:28:28ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2502612Deformable cylinder extraction from LiDAR point cloud using candidate selectionMeng Du0Di Cao1Cheng Wang2Sheng Nie3Jingru Wang4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaCylinder extraction is a fundamental task in point cloud-based environmental mapping such as tree modeling and reverse engineering. However, current methods are hindered by data missing, and their performance on deformable cylinders remains to be improved. To address these challenges, this paper proposes an unsupervised approach for robustly extracting deformable cylinders using a candidate selection strategy. A deformable cylinder is represented by a skeleton and cross-sectional circles. First, preprocessing is performed to downsample the raw point cloud and estimate normals. Second, cylinder extraction is conducted to obtain the initial cylinder segments. Then, candidate selection is applied to evolve the initial cylinder segments, extending the skeletons and generating cross-sectional circles to form the initial deformable cylinders. Finally, a refinement step is performed to optimize these cylinders. Experimental results validate the overall effectiveness and robustness of the algorithm against data missing. The average precision, recall, and F1 score are 0.95, 0.90, and 0.92, respectively, in the point cloud collected from four real-world pipeline scenarios. This algorithm supports practical applications such as clearance space measurement and Building Information Modeling (BIM) generation for pipeline systems.https://www.tandfonline.com/doi/10.1080/17538947.2025.2502612Candidate selectiondeformable cylinder extractionpoint clouddata missing |
| spellingShingle | Meng Du Di Cao Cheng Wang Sheng Nie Jingru Wang Deformable cylinder extraction from LiDAR point cloud using candidate selection International Journal of Digital Earth Candidate selection deformable cylinder extraction point cloud data missing |
| title | Deformable cylinder extraction from LiDAR point cloud using candidate selection |
| title_full | Deformable cylinder extraction from LiDAR point cloud using candidate selection |
| title_fullStr | Deformable cylinder extraction from LiDAR point cloud using candidate selection |
| title_full_unstemmed | Deformable cylinder extraction from LiDAR point cloud using candidate selection |
| title_short | Deformable cylinder extraction from LiDAR point cloud using candidate selection |
| title_sort | deformable cylinder extraction from lidar point cloud using candidate selection |
| topic | Candidate selection deformable cylinder extraction point cloud data missing |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2502612 |
| work_keys_str_mv | AT mengdu deformablecylinderextractionfromlidarpointcloudusingcandidateselection AT dicao deformablecylinderextractionfromlidarpointcloudusingcandidateselection AT chengwang deformablecylinderextractionfromlidarpointcloudusingcandidateselection AT shengnie deformablecylinderextractionfromlidarpointcloudusingcandidateselection AT jingruwang deformablecylinderextractionfromlidarpointcloudusingcandidateselection |