Color Point Cloud Registration Based on Supervoxel Correspondence
With the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8950119/ |
| _version_ | 1818910146048294912 |
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| author | Yang Yang Weile Chen Muyi Wang Dexing Zhong Shaoyi Du |
| author_facet | Yang Yang Weile Chen Muyi Wang Dexing Zhong Shaoyi Du |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | With the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud. And these features are extracted from point clouds based on the supervoxel segmentation. By jointly conducting these features for similarity measure, a weight parameter is dynamically adapted between the color and the spatial information. The registration algorithm is under a classic iterative framework for building the correspondence and estimating transformation parameters. In addition, we provide a mutual correspondence matching condition with hybrid features to build some more robust relationships for estimating transformation parameters. Experimental results demonstrate that our method can effectively reduce the number of point data for registration and achieve good matching results even in a poor initial condition. |
| first_indexed | 2024-12-19T22:38:10Z |
| format | Article |
| id | doaj.art-dbe014e87ac047d39155582e35e38747 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:38:10Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dbe014e87ac047d39155582e35e387472022-12-21T20:03:09ZengIEEEIEEE Access2169-35362020-01-0187362737210.1109/ACCESS.2020.29639878950119Color Point Cloud Registration Based on Supervoxel CorrespondenceYang Yang0https://orcid.org/0000-0001-8687-4427Weile Chen1https://orcid.org/0000-0001-5679-2332Muyi Wang2https://orcid.org/0000-0003-1766-827XDexing Zhong3https://orcid.org/0000-0002-6806-6300Shaoyi Du4https://orcid.org/0000-0002-7092-0596School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Software Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaShenzhen Research School, Xi’an Jiaotong University, Shenzhen, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaWith the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud. And these features are extracted from point clouds based on the supervoxel segmentation. By jointly conducting these features for similarity measure, a weight parameter is dynamically adapted between the color and the spatial information. The registration algorithm is under a classic iterative framework for building the correspondence and estimating transformation parameters. In addition, we provide a mutual correspondence matching condition with hybrid features to build some more robust relationships for estimating transformation parameters. Experimental results demonstrate that our method can effectively reduce the number of point data for registration and achieve good matching results even in a poor initial condition.https://ieeexplore.ieee.org/document/8950119/Color point cloud registrationhybrid featuremutual correspondence matching |
| spellingShingle | Yang Yang Weile Chen Muyi Wang Dexing Zhong Shaoyi Du Color Point Cloud Registration Based on Supervoxel Correspondence IEEE Access Color point cloud registration hybrid feature mutual correspondence matching |
| title | Color Point Cloud Registration Based on Supervoxel Correspondence |
| title_full | Color Point Cloud Registration Based on Supervoxel Correspondence |
| title_fullStr | Color Point Cloud Registration Based on Supervoxel Correspondence |
| title_full_unstemmed | Color Point Cloud Registration Based on Supervoxel Correspondence |
| title_short | Color Point Cloud Registration Based on Supervoxel Correspondence |
| title_sort | color point cloud registration based on supervoxel correspondence |
| topic | Color point cloud registration hybrid feature mutual correspondence matching |
| url | https://ieeexplore.ieee.org/document/8950119/ |
| work_keys_str_mv | AT yangyang colorpointcloudregistrationbasedonsupervoxelcorrespondence AT weilechen colorpointcloudregistrationbasedonsupervoxelcorrespondence AT muyiwang colorpointcloudregistrationbasedonsupervoxelcorrespondence AT dexingzhong colorpointcloudregistrationbasedonsupervoxelcorrespondence AT shaoyidu colorpointcloudregistrationbasedonsupervoxelcorrespondence |