Show simple item record

dc.contributor.authorPatrick Mullenen_US
dc.contributor.authorFernando de Goesen_US
dc.contributor.authorMathieu Desbrunen_US
dc.contributor.authorDavid Cohen-Steineren_US
dc.contributor.authorPierre Alliezen_US
dc.date.accessioned2015-02-23T17:15:44Z
dc.date.available2015-02-23T17:15:44Z
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10.2312/CGF.v29i5pp1733-1741en_US
dc.identifier.urihttp://hdl.handle.net/10.2312/CGF.v29i5pp1733-1741
dc.description.abstractWe propose a modular framework for robust 3D reconstruction from unorganized, unoriented, noisy, and outlierridden geometric data. We gain robustness and scalability over previous methods through an unsigned distance approximation to the input data followed by a global stochastic signing of the function. An isosurface reconstruction is finally deduced via a sparse linear solve. We show with experiments on large, raw, geometric datasets that this approach is scalable while robust to noise, outliers, and holes. The modularity of our approach facilitates customization of the pipeline components to exploit specific idiosyncracies of datasets, while the simplicity of each component leads to a straightforward implementation.en_US
dc.titleSigning the Unsigned: Robust Surface Reconstruction from Raw Pointsetsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume29en_US
dc.description.number5en_US
dc.identifier.doi10.1111/j.1467-8659.2010.01782.xen_US
dc.identifier.pages1733-1741en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record