Surface Reconstruction with Enriched Reproducing Kernel Particle Approximation
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Date
2005
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
There 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.
Description
@inproceedings{:10.2312/SPBG/SPBG05/079-087,
booktitle = {Eurographics Symposium on Point-Based Graphics (2005)},
editor = {Marc Alexa and Szymon Rusinkiewicz and Mark Pauly and Matthias Zwicker},
title = {{Surface Reconstruction with Enriched Reproducing Kernel Particle Approximation}},
author = {Reuter, Patrick and Joyot, Pierre and Trunzler, Jean and Boubekeur, Tamy and Schlick, Christophe},
year = {2005},
publisher = {The Eurographics Association},
ISSN = {1811-7813},
ISBN = {3-905673-20-7},
DOI = {/10.2312/SPBG/SPBG05/079-087}
}