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dc.contributor.authorHou, Feien_US
dc.contributor.authorWang, Chiyuen_US
dc.contributor.authorWang, Wenchengen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.authorQian, Chenen_US
dc.contributor.authorHe, Yingen_US
dc.identifier.citationHou, F., Wang, C., Wang, W., Qin, H., Qian, C. & He, Y. (2022). Iterative poisson surface reconstruction (iPSR) for unoriented points. ACM Transactions On Graphics, 41(4), 128-.
dc.description.abstractPoisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.rights© 2022 The owner/author(s). All rights reserved.en_US
dc.subjectComputer Science - Graphicsen_US
dc.titleIterative poisson surface reconstruction (iPSR) for unoriented pointsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsUnoriented Pointsen_US
dc.subject.keywordsIterative Algorithmen_US
dc.description.acknowledgementThis research has been partially supported by National Natural Science Foundation of China (61872347, 62072446), Special Plan for the Development of Distinguished Young Scientists of ISCAS (Y8RC535018), National Science Foundation (IIS-1715985 & 1812606 to Qin), Singapore Ministry of Education (MOE-T2EP20220-0005 and RG20/20) and RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).en_US
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