Information
- Publication Type: Journal Paper with Conference Talk
- Workgroup(s)/Project(s):
- Date: August 2014
- Journal: ACM Transactions on Graphics (Proc. of ACM SIGGRAPH 2014)
- Volume: 33
- Number: 4
- Location: Vancouver, Canada
- Lecturer: Reinhold Preiner
- ISSN: 0730-0301
- Event: ACM SIGGRAPH 2014
- DOI: 10.1145/2601097.2601172
- Conference date: 10. August 2014 – 14. August 2014
- Pages: 47:1 – 47:13
- Keywords: point set, Gaussian mixture, Hierarchical EM, upsampling, dynamic reconstruction, L1 reconstruction
Abstract
With better and faster acquisition devices comes a demand for fast robust reconstruction algorithms, but no L1-based technique has been fast enough for online use so far. In this paper, we present a novel continuous formulation of the weighted locally optimal projection (WLOP) operator based on a Gaussian mixture describing the input point density. Our method is up to 7 times faster than an optimized GPU implementation of WLOP, and achieves interactive frame rates for moderately sized point clouds. We give a comprehensive quality analysis showing that our continuous operator achieves a generally higher reconstruction quality than its discrete counterpart. Additionally, we show how to apply our continuous formulation to spherical mixtures of normal directions, to also achieve a fast robust normal reconstruction.Project Page: https://www.cg.tuwien.ac.at/~preiner/projects/clop/
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BibTeX
@article{preiner2014clop,
title = "Continuous Projection for Fast L1 Reconstruction",
author = "Reinhold Preiner and Oliver Mattausch and Murat Arikan and
Renato Pajarola and Michael Wimmer",
year = "2014",
abstract = "With better and faster acquisition devices comes a demand
for fast robust reconstruction algorithms, but no L1-based
technique has been fast enough for online use so far. In
this paper, we present a novel continuous formulation of the
weighted locally optimal projection (WLOP) operator based on
a Gaussian mixture describing the input point density. Our
method is up to 7 times faster than an optimized GPU
implementation of WLOP, and achieves interactive frame rates
for moderately sized point clouds. We give a comprehensive
quality analysis showing that our continuous operator
achieves a generally higher reconstruction quality than its
discrete counterpart. Additionally, we show how to apply our
continuous formulation to spherical mixtures of normal
directions, to also achieve a fast robust normal
reconstruction. Project Page:
https://www.cg.tuwien.ac.at/~preiner/projects/clop/",
month = aug,
journal = "ACM Transactions on Graphics (Proc. of ACM SIGGRAPH 2014)",
volume = "33",
number = "4",
issn = "0730-0301",
doi = "10.1145/2601097.2601172",
pages = "47:1--47:13",
keywords = "point set, Gaussian mixture, Hierarchical EM, upsampling,
dynamic reconstruction, L1 reconstruction",
URL = "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/",
}