Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds
Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. T...
Published in: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Online Access: | https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024 https://isprs-annals.copernicus.org/articles/X-2-2024/113/2024/ |
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ftcopernicus:oai:publications.copernicus.org:isprs-annals120938 2024-06-23T07:53:04+00:00 Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds Isfort, Steffen Elias, Melanie Maas, Hans-Gerd 2024-06-09 application/pdf https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024 https://isprs-annals.copernicus.org/articles/X-2-2024/113/2024/ eng eng doi:10.5194/isprs-annals-X-2-2024-113-2024 https://isprs-annals.copernicus.org/articles/X-2-2024/113/2024/ eISSN: 2194-9050 Text 2024 ftcopernicus https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024 2024-06-13T01:24:17Z Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector. Text glacier Copernicus Publications: E-Journals Norway ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-2-2024 113 120 |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector. |
format |
Text |
author |
Isfort, Steffen Elias, Melanie Maas, Hans-Gerd |
spellingShingle |
Isfort, Steffen Elias, Melanie Maas, Hans-Gerd Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
author_facet |
Isfort, Steffen Elias, Melanie Maas, Hans-Gerd |
author_sort |
Isfort, Steffen |
title |
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
title_short |
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
title_full |
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
title_fullStr |
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
title_full_unstemmed |
Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds |
title_sort |
development and evaluation of a two-staged 3d keypoint based workflow for the co-registration of unstructured multi-temporal and multi-modal 3d point clouds |
publishDate |
2024 |
url |
https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024 https://isprs-annals.copernicus.org/articles/X-2-2024/113/2024/ |
geographic |
Norway |
geographic_facet |
Norway |
genre |
glacier |
genre_facet |
glacier |
op_source |
eISSN: 2194-9050 |
op_relation |
doi:10.5194/isprs-annals-X-2-2024-113-2024 https://isprs-annals.copernicus.org/articles/X-2-2024/113/2024/ |
op_doi |
https://doi.org/10.5194/isprs-annals-X-2-2024-113-2024 |
container_title |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
X-2-2024 |
container_start_page |
113 |
op_container_end_page |
120 |
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