Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures
The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D repr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/18/2873 |
| _version_ | 1797416643620503552 |
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| author | Elisa Mariarosaria Farella Alessandro Torresani Fabio Remondino |
| author_facet | Elisa Mariarosaria Farella Alessandro Torresani Fabio Remondino |
| author_sort | Elisa Mariarosaria Farella |
| collection | DOAJ |
| description | The paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub. |
| first_indexed | 2024-03-09T06:07:09Z |
| format | Article |
| id | doaj.art-48450a2db7454aa0908293efb0b079a0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T06:07:09Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-48450a2db7454aa0908293efb0b079a02023-12-03T12:02:30ZengMDPI AGRemote Sensing2072-42922020-09-011218287310.3390/rs12182873Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality MeasuresElisa Mariarosaria Farella0Alessandro Torresani1Fabio Remondino23D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive, 18, 38123 Trento, Italy3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive, 18, 38123 Trento, Italy3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Via Sommarive, 18, 38123 Trento, ItalyThe paper presents an efficient photogrammetric workflow to improve the 3D reconstruction of scenes surveyed by integrating terrestrial and Unmanned Aerial Vehicle (UAV) images. In the last years, the integration of this kind of images has shown clear advantages for the complete and detailed 3D representation of large and complex scenarios. Nevertheless, their photogrammetric integration often raises several issues in the image orientation and dense 3D reconstruction processes. Noisy and erroneous 3D reconstructions are the typical result of inaccurate orientation results. In this work, we propose an automatic filtering procedure which works at the sparse point cloud level and takes advantage of photogrammetric quality features. The filtering step removes low-quality 3D tie points before refining the image orientation in a new adjustment and generating the final dense point cloud. Our method generalizes to many datasets, as it employs statistical analyses of quality feature distributions to identify suitable filtering thresholds. Reported results show the effectiveness and reliability of the method verified using both internal and external quality checks, as well as visual qualitative comparisons. We made the filtering tool publicly available on GitHub.https://www.mdpi.com/2072-4292/12/18/2873data fusionsparse point cloudfilteringimage orientationdense point cloud generation |
| spellingShingle | Elisa Mariarosaria Farella Alessandro Torresani Fabio Remondino Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures Remote Sensing data fusion sparse point cloud filtering image orientation dense point cloud generation |
| title | Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures |
| title_full | Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures |
| title_fullStr | Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures |
| title_full_unstemmed | Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures |
| title_short | Refining the Joint 3D Processing of Terrestrial and UAV Images Using Quality Measures |
| title_sort | refining the joint 3d processing of terrestrial and uav images using quality measures |
| topic | data fusion sparse point cloud filtering image orientation dense point cloud generation |
| url | https://www.mdpi.com/2072-4292/12/18/2873 |
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