Robust pose estimation which guarantees positive depths
Abstract In the area of 3D computer vision, the ability to estimate pose between two cameras under high noise levels while maintaining small reprojection errors reflects the robustness of such pose estimation algorithms. Moreover, maintaining positive depth constraint is another challenging task. Un...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49553-9 |
| _version_ | 1797388417583022080 |
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| author | Chun Li John E. McInroy |
| author_facet | Chun Li John E. McInroy |
| author_sort | Chun Li |
| collection | DOAJ |
| description | Abstract In the area of 3D computer vision, the ability to estimate pose between two cameras under high noise levels while maintaining small reprojection errors reflects the robustness of such pose estimation algorithms. Moreover, maintaining positive depth constraint is another challenging task. Unfortunately, current pose estimation algorithms are often sensitive to noise/outliers and do not always guarantee positive depths. As a standalone task, these algorithms perform a positive sign check and simply discard the points with negative depths after the algorithms are executed. These algorithms do not integrate positive depth constraints into the algorithms themselves. Instead, they do it afterwards. Here, from a comprehensive mathematical derivation, we propose a novel pose estimation algorithm that integrates positive depth constraint into the algorithm itself by estimating the depths directly. The algorithm was competitive in producing small reprojection errors when compared to the state-of-the-art algorithms under both synthetic and real-world tests, while most importantly guaranteeing positive depths. |
| first_indexed | 2024-03-08T22:40:32Z |
| format | Article |
| id | doaj.art-92d0ad6741a64a9eb26ed22267875c7f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:40:32Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-92d0ad6741a64a9eb26ed22267875c7f2023-12-17T12:13:34ZengNature PortfolioScientific Reports2045-23222023-12-0113113310.1038/s41598-023-49553-9Robust pose estimation which guarantees positive depthsChun Li0John E. McInroy1Department of Electrical and Computer Engineering, University of WyomingDepartment of Electrical and Computer Engineering, University of WyomingAbstract In the area of 3D computer vision, the ability to estimate pose between two cameras under high noise levels while maintaining small reprojection errors reflects the robustness of such pose estimation algorithms. Moreover, maintaining positive depth constraint is another challenging task. Unfortunately, current pose estimation algorithms are often sensitive to noise/outliers and do not always guarantee positive depths. As a standalone task, these algorithms perform a positive sign check and simply discard the points with negative depths after the algorithms are executed. These algorithms do not integrate positive depth constraints into the algorithms themselves. Instead, they do it afterwards. Here, from a comprehensive mathematical derivation, we propose a novel pose estimation algorithm that integrates positive depth constraint into the algorithm itself by estimating the depths directly. The algorithm was competitive in producing small reprojection errors when compared to the state-of-the-art algorithms under both synthetic and real-world tests, while most importantly guaranteeing positive depths.https://doi.org/10.1038/s41598-023-49553-9 |
| spellingShingle | Chun Li John E. McInroy Robust pose estimation which guarantees positive depths Scientific Reports |
| title | Robust pose estimation which guarantees positive depths |
| title_full | Robust pose estimation which guarantees positive depths |
| title_fullStr | Robust pose estimation which guarantees positive depths |
| title_full_unstemmed | Robust pose estimation which guarantees positive depths |
| title_short | Robust pose estimation which guarantees positive depths |
| title_sort | robust pose estimation which guarantees positive depths |
| url | https://doi.org/10.1038/s41598-023-49553-9 |
| work_keys_str_mv | AT chunli robustposeestimationwhichguaranteespositivedepths AT johnemcinroy robustposeestimationwhichguaranteespositivedepths |