Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163337
Title: Iterative poisson surface reconstruction (iPSR) for unoriented points
Authors: Hou, Fei
Wang, Chiyu
Wang, Wencheng
Qin, Hong
Qian, Chen
He, Ying
Keywords: Computer Science - Graphics
Issue Date: 2022
Source: Hou, 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-. https://dx.doi.org/10.1145/3528223.3530096
Project: MOE-T2EP20220-0005
RG20/20)
IAF-ICP
Journal: ACM Transactions on Graphics
Abstract: Poisson 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 https://github.com/houfei0801/ipsr.
URI: https://hdl.handle.net/10356/163337
ISSN: 0730-0301
DOI: 10.1145/3528223.3530096
Rights: © 2022 The owner/author(s). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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