Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space
The emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry-based reconstruction has been studied intensively...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4289 |
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| author | Hong Huang Zehao Ye Cheng Zhang Yong Yue Chunyi Cui Amin Hammad |
| author_facet | Hong Huang Zehao Ye Cheng Zhang Yong Yue Chunyi Cui Amin Hammad |
| author_sort | Hong Huang |
| collection | DOAJ |
| description | The emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry-based reconstruction has been studied intensively in the literature, the problem remains in evaluating the accuracy of the generated point cloud in practice. Typically, checking the coordinates of ground control points (GCPs) using a total station is considered a promising approach; however, the GCPs have clear and identifiable features and consistent normal vectors or less roughness, which cannot be considered as a typical sample for an accuracy evaluation of the point cloud. Meanwhile, the cloud-to-cloud (C2C) and cloud-to-mesh (C2M) comparison methods usually consider either the closest point or the neighboring points within a fixed searching radius as the “ground truth”, which may not reflect the actual accuracy; therefore, the present paper proposes an adaptive cloud-to-cloud (AC2C) comparison method to search the potential “ground truth” in the theoretical error space. The theoretical error space of each point is estimated according to the position of the corresponding visible cameras and their distances to a target point. A case study is carried out to investigate the feasibility of the proposed AC2C comparison method. The results presented basically the same error distribution range from 0 to 20 mm with the C2C and C2M methods, but with a higher mean value and a much smaller standard deviation. Compared to the existing methods, the proposed method provides new thinking in evaluating the accuracy of SfM-MVS by including the theoretical error constraints. |
| first_indexed | 2024-03-10T01:19:13Z |
| format | Article |
| id | doaj.art-9fcef9cf8c19468088cdfe455c23c30b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:19:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9fcef9cf8c19468088cdfe455c23c30b2023-11-23T14:04:07ZengMDPI AGRemote Sensing2072-42922022-08-011417428910.3390/rs14174289Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error SpaceHong Huang0Zehao Ye1Cheng Zhang2Yong Yue3Chunyi Cui4Amin Hammad5Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaSuzhou Erjian Construction Group Co., Ltd., Suzhou 215122, ChinaDepartment of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Civil Engineering, Dalian Maritime University, Dalian 116026, ChinaConcordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 2W1, CanadaThe emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry-based reconstruction has been studied intensively in the literature, the problem remains in evaluating the accuracy of the generated point cloud in practice. Typically, checking the coordinates of ground control points (GCPs) using a total station is considered a promising approach; however, the GCPs have clear and identifiable features and consistent normal vectors or less roughness, which cannot be considered as a typical sample for an accuracy evaluation of the point cloud. Meanwhile, the cloud-to-cloud (C2C) and cloud-to-mesh (C2M) comparison methods usually consider either the closest point or the neighboring points within a fixed searching radius as the “ground truth”, which may not reflect the actual accuracy; therefore, the present paper proposes an adaptive cloud-to-cloud (AC2C) comparison method to search the potential “ground truth” in the theoretical error space. The theoretical error space of each point is estimated according to the position of the corresponding visible cameras and their distances to a target point. A case study is carried out to investigate the feasibility of the proposed AC2C comparison method. The results presented basically the same error distribution range from 0 to 20 mm with the C2C and C2M methods, but with a higher mean value and a much smaller standard deviation. Compared to the existing methods, the proposed method provides new thinking in evaluating the accuracy of SfM-MVS by including the theoretical error constraints.https://www.mdpi.com/2072-4292/14/17/4289error estimationSfM-MVScloud-to-cloud comparisonphotogrammetric point cloud |
| spellingShingle | Hong Huang Zehao Ye Cheng Zhang Yong Yue Chunyi Cui Amin Hammad Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space Remote Sensing error estimation SfM-MVS cloud-to-cloud comparison photogrammetric point cloud |
| title | Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space |
| title_full | Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space |
| title_fullStr | Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space |
| title_full_unstemmed | Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space |
| title_short | Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space |
| title_sort | adaptive cloud to cloud ac2c comparison method for photogrammetric point cloud error estimation considering theoretical error space |
| topic | error estimation SfM-MVS cloud-to-cloud comparison photogrammetric point cloud |
| url | https://www.mdpi.com/2072-4292/14/17/4289 |
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