Please use this identifier to cite or link to this item: https://bura.brunel.ac.uk/handle/2438/33881
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dc.contributor.authorZhang, Shaojie-
dc.contributor.authorWang, Yinghui-
dc.contributor.authorNan, Bin-
dc.contributor.authorLi, Wei-
dc.contributor.authorYang, Jinlong-
dc.contributor.authorYan, Tao-
dc.contributor.authorWang, Yukai-
dc.contributor.authorHuang, Liangyi-
dc.contributor.authorWang, Mingfeng-
dc.contributor.authorAtadjanov, Ibragim R-
dc.date.accessioned2026-09-18T13:05:06Z-
dc.date.available2026-09-18T13:05:06Z-
dc.date.issued2024-02-25-
dc.identifier.citationZhang, S. et al. (2024) ‘A Robust Error-Resistant View Selection Method for 3D Reconstruction’ [Version 2 Feb 2024]. Available at: https://doi.org/10.48550/arXiv.2402.11431en_US
dc.identifier.otherhttps://arxiv.org/abs/2402.11431-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/33881-
dc.descriptionPreprint submitted to arxiv. Cite as: arXiv:2402.11431v2 [cs.CV]en_US
dc.description.abstractTo address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-resistant matrix. The sorting results of each row in the error-resistant matrix determine the candidate view set for each view. By traversing the candidate view sets of all views and completing the missing views based on the error-resistant matrix, the integrity of 3D reconstruction is ensured. Experimental comparisons between this method and the exhaustive method with the highest accuracy in the COLMAP program are conducted in terms of average reprojection error and absolute trajectory error in the reconstruction results. The proposed method demonstrates an average reduction of 29.40% in reprojection error accuracy and 5.07% in absolute trajectory error on the TUM dataset and DTU dataset.en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China (No. 62172190), National Key Research and Development Program (No. 2023YFC3805901), the "Double Creation" Plan of Jiangsu Province (Certificate: JSSCRC2021532) and the "Taihu Talent-Innovative Leading Talent" Plan of Wuxi City(Certificate Date: 202110).en_US
dc.format.extentpp. 1–8-
dc.format.mediumElectronic-
dc.languageEnglishen_US
dc.language.isoen_USen_US
dc.publisherarXiven_US
dc.rightsRe-use licence for this version: arXiv.org - Non-exclusive license to distribute-
dc.rights.urihttps://arxiv.org/licenses/nonexclusive-distrib/1.0/-
dc.subjectview selectionen_US
dc.subjectcamera baselineen_US
dc.subjectcamera baselineen_US
dc.subjecttriangulationen_US
dc.subjecterror resistanceen_US
dc.subjectComputer Vision and Pattern Recognition (cs.CV)-
dc.titleA Robust Error-Resistant View Selection Method for 3D Reconstructionen_US
dc.typePreprinten_US
dc.date.dateAccepted2024-02-25-
dc.identifier.doihttps://doi.org/10.48550/arXiv.2402.11431-
dc.identifier.eissn2331-8422-
dcterms.isPartOfarXiven_US
dcterms.issued2024-02-25-
dc.date.updated2026-09-18T12:57:04Z-
dc.rights.holderThe Authors-
dc.contributor.orcidWang, Mingfeng [0000-0001-6551-0325]-
Appears in Collections:Department of Mechanical and Aerospace Engineering Research Papers

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