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Mark Pauly, Richard Keiser, Markus Gross, Multi-Scale Feature Extraction on Point-Sampled Models, Proceedings of Eurographics 2003
Abstract:
We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructured
point cloud as input, our method first applies principal component analysis on local neighborhoods to
classify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we then
compute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the features
while maintaining a close connection to the underlying surface, we use an adaptation of active contour models.
Central to our method is a multi-scale classification operator that allows feature analysis at multiple
scales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves the
reliability of the detection phase and makes our method more robust in the presence of noise. To illustrate the
usefulness of our method, we have implemented a non-photorealistic point renderer to visualize point-sampled
surfaces as line drawings of their extracted feature curves.
Bibtex:
@inproceedings{pkg-msfepsm-03,
author = {Mark Pauly and Richard Keiser and Markus Gross },
title = {Multi-Scale Feature Extraction on Point-Sampled Models},
booktitle = {Proceedings of Eurographics},
year = {2003},
}
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