Surface Reconstruction with Enriched Reproducing Kernel Particle Approximation

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dc.contributor.authorReuter, Patricken_US
dc.contributor.authorJoyot, Pierreen_US
dc.contributor.authorTrunzler, Jeanen_US
dc.contributor.authorBoubekeur, Tamyen_US
dc.contributor.authorSchlick, Christopheen_US
dc.contributor.editorMarc Alexa and Szymon Rusinkiewicz and Mark Pauly and Matthias Zwickeren_US
dc.date.accessioned2014-01-29T16:31:43Z
dc.date.available2014-01-29T16:31:43Z
dc.date.issued2005en_US
dc.description.abstractThere are many techniques that reconstruct continuous 3D surfaces from scattered point data coming from laser range scanners. One of the most commonly used representations are Point Set Surfaces (PSS) defined as the set of stationary points of a Moving Least Squares (MLS) projection operator. One interesting property of the MLS projection is to automatically filter out high frequency noise, that is usually present in raw data due to scanning errors. Unfortunately, the MLS projection also smoothes out any high frequency feature, such as creases or corners, that may be present in the scanned geometry, and does not offer any possibility to distinguish between such feature and noise. The main contribution of this paper, is to present an alternative projection operator for surface reconstruction, based on the Enriched Reproducing Kernel Particle Approximation (ERKPA), which allows the reconstruction process to account for high frequency features, by letting the user explicitly tag the corresponding areas of the scanned geometry.en_US
dc.description.seriesinformationEurographics Symposium on Point-Based Graphics (2005)en_US
dc.identifier.isbn3-905673-20-7en_US
dc.identifier.issn1811-7813en_US
dc.identifier.urihttps://doi.org/10.2312/SPBG/SPBG05/079-087en_US
dc.publisherThe Eurographics Associationen_US
dc.titleSurface Reconstruction with Enriched Reproducing Kernel Particle Approximationen_US
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