Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor
Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/20/5119 |
| _version_ | 1797470081997864960 |
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| author | Yusheng Yang Guorun Fang Zhonghua Miao Yangmin Xie |
| author_facet | Yusheng Yang Guorun Fang Zhonghua Miao Yangmin Xie |
| author_sort | Yusheng Yang |
| collection | DOAJ |
| description | Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level. |
| first_indexed | 2024-03-09T19:31:41Z |
| format | Article |
| id | doaj.art-6bb47e02a8a2454893013391ea4ca167 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T19:31:41Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6bb47e02a8a2454893013391ea4ca1672023-11-24T02:19:32ZengMDPI AGRemote Sensing2072-42922022-10-011420511910.3390/rs14205119Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric DescriptorYusheng Yang0Guorun Fang1Zhonghua Miao2Yangmin Xie3School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaAligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level.https://www.mdpi.com/2072-4292/14/20/5119semantic–geometric descriptorwindow and door detectionimproved Hungarian algorithm |
| spellingShingle | Yusheng Yang Guorun Fang Zhonghua Miao Yangmin Xie Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor Remote Sensing semantic–geometric descriptor window and door detection improved Hungarian algorithm |
| title | Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor |
| title_full | Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor |
| title_fullStr | Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor |
| title_full_unstemmed | Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor |
| title_short | Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor |
| title_sort | indoor outdoor point cloud alignment using semantic geometric descriptor |
| topic | semantic–geometric descriptor window and door detection improved Hungarian algorithm |
| url | https://www.mdpi.com/2072-4292/14/20/5119 |
| work_keys_str_mv | AT yushengyang indooroutdoorpointcloudalignmentusingsemanticgeometricdescriptor AT guorunfang indooroutdoorpointcloudalignmentusingsemanticgeometricdescriptor AT zhonghuamiao indooroutdoorpointcloudalignmentusingsemanticgeometricdescriptor AT yangminxie indooroutdoorpointcloudalignmentusingsemanticgeometricdescriptor |