<div class="csl-bib-body">
<div class="csl-entry">Erler, P., Fuentes‐Perez, L., Hermosilla, P., Guerrero, P., Pajarola, R., & Wimmer, M. (2024). PPSurf: combining patches and point convolutions for detailed surface reconstruction. <i>Computer Graphics Forum</i>, Article e15000. https://doi.org/10.1111/cgf.15000</div>
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dc.identifier.issn
0167-7055
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192690
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dc.description.abstract
3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage and engineering. Current approaches either try to optimize a non-data-driven surface representation to fit the points, or learn a data-driven prior over the distribution of commonly occurring surfaces and how they correlate with potentially noisy point clouds. Data-driven methods enable robust handling of noise and typically either focus on a global or a local prior, which trade-off between robustness to noise on the global end and surface detail preservation on the local end. We propose PPSurf as a method that combines a global prior based on point convolutions and a local prior based on processing local point cloud patches. We show that this approach is robust to noise while recovering surface details more accurately than the current state-of-the-art. Our source code, pre-trained model and dataset are available at https://github.com/cg-tuwien/ppsurf
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.publisher
WILEY
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dc.relation.ispartof
Computer Graphics Forum
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
modeling
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dc.subject
surface reconstruction
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dc.title
PPSurf: combining patches and point convolutions for detailed surface reconstruction