IRLTrees3D: A 3D reconstruction dataset of trees

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dc.contributor.authorChai, Josephen
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderResearch Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-12-23T10:13:44Z
dc.date.available2025-12-23T10:13:44Z
dc.date.issued2025en
dc.description.abstractMeasuring tree dimensions is fundamentally important to effective forestry and environmental monitoring, particularly in the estimation of investable carbon stock. Three dimensional (3D) reconstruction, therefore, has become promising for enabling high-precision measurements and AI-driven applications in this domain, but remains challenging due to environmental uncertainty and the limitations of existing methods and datasets. This paper presents a novel, high-resolution dataset of 3D vegetation models comprising trees, logs, and plants captured from diverse locations across Ireland. The reconstruction pipeline with high-quality 2D views and 3D artifacts is provided to ensure methodological transparency and reproducibility for bridging multidimensional AI and computer vision tasks. To evaluate the geometric accuracy of the reconstructed models, we employed the diameter at breast height (DBH) as a validation metric. Our evaluation yielded a mean absolute error of 0.53 cm, with a standard deviation of 0.28cm, demonstrating the reliability of our cost-effective photogrammetry approach. The release of this dataset constitutes a significant contribution to the domain of 3D reconstruction of trees, providing a robust framework for future research in the field.en
dc.description.sponsorshipResearch Ireland (12-RC-2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationChai, J., O'Sullivan, B. and Nguyen, H. D. (2025) 'IRLTrees3D: A 3D reconstruction dataset of trees', Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Honolulu, Hawai'i, 19-23 October 2025, pp. 2876-2881. Available at: https://openaccess.thecvf.com/content/ICCV2025W/SEA/html/Chai_IRLTrees3D_A_3D_Reconstruction_Dataset_of_Trees_ICCVW_2025_paper.html (Accessed: 23 December 2025)en
dc.identifier.endpage2881en
dc.identifier.startpage2876en
dc.identifier.urihttps://hdl.handle.net/10468/18362
dc.language.isoenen
dc.publisherComputer Vision Foundationen
dc.relation.ispartofIEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Honolulu, Hawai'i, 19-23 October 2025en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.project12-RC-2289-P2en
dc.relation.urihttps://openaccess.thecvf.com/content/ICCV2025W/SEA/html/Chai_IRLTrees3D_A_3D_Reconstruction_Dataset_of_Trees_ICCVW_2025_paper.htmlen
dc.rights© 2025, the Authors.en
dc.subjectMeasuring tree dimensionsen
dc.subjectThree dimensional (3D) reconstructionen
dc.titleIRLTrees3D: A 3D reconstruction dataset of treesen
dc.typeConference itemen
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