Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds
Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/24/4737 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850058485180399616 |
|---|---|
| author | Loïca Avanthey Laurent Beaudoin |
| author_facet | Loïca Avanthey Laurent Beaudoin |
| author_sort | Loïca Avanthey |
| collection | DOAJ |
| description | Assessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory. |
| format | Article |
| id | doaj-art-a7ba24f7e7124ecdaa25cb74359392ea |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a7ba24f7e7124ecdaa25cb74359392ea2025-08-20T02:51:07ZengMDPI AGRemote Sensing2072-42922024-12-011624473710.3390/rs16244737Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local CloudsLoïca Avanthey0Laurent Beaudoin1Epita Research Laboratory, EPITA Computer Engineering School, 94270 Le Kremlin-Bicêtre, FranceEpita Research Laboratory, EPITA Computer Engineering School, 94270 Le Kremlin-Bicêtre, FranceAssessing the completeness of an underwater 3D reconstruction on-site is crucial as it allows for rescheduling acquisitions, which capture missing data during a mission, avoiding additional costs of a subsequent mission. This assessment needs to rely on a dense point cloud since a sparse cloud lacks detail and a triangulated model can hide gaps. The challenge is to generate a dense cloud with field-deployable tools. Traditional dense reconstruction methods can take several dozen hours on low-capacity systems like laptops or embedded units. To speed up this process, we propose building the dense cloud incrementally within an SfM framework while incorporating data redundancy management to eliminate recalculations and filtering already-processed data. The method evaluates overlap area limits and computes depths by propagating the matching around SeaPoints—the keypoints we design for identifying reliable areas regardless of the quality of the processed underwater images. This produces local partial dense clouds, which are aggregated into a common frame via the SfM pipeline to produce the global dense cloud. Compared to the production of complete dense local clouds, this approach reduces the computation time by about 70% while maintaining a comparable final density. The underlying prospect of this work is to enable real-time completeness estimation directly on board, allowing for the dynamic re-planning of the acquisition trajectory.https://www.mdpi.com/2072-4292/16/24/4737underwater 3D reconstructionpartial reconstructioncompletenessSfMseabed digital twin |
| spellingShingle | Loïca Avanthey Laurent Beaudoin Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds Remote Sensing underwater 3D reconstruction partial reconstruction completeness SfM seabed digital twin |
| title | Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds |
| title_full | Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds |
| title_fullStr | Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds |
| title_full_unstemmed | Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds |
| title_short | Dense In Situ Underwater 3D Reconstruction by Aggregation of Successive Partial Local Clouds |
| title_sort | dense in situ underwater 3d reconstruction by aggregation of successive partial local clouds |
| topic | underwater 3D reconstruction partial reconstruction completeness SfM seabed digital twin |
| url | https://www.mdpi.com/2072-4292/16/24/4737 |
| work_keys_str_mv | AT loicaavanthey denseinsituunderwater3dreconstructionbyaggregationofsuccessivepartiallocalclouds AT laurentbeaudoin denseinsituunderwater3dreconstructionbyaggregationofsuccessivepartiallocalclouds |