Self-similarity for Accurate Compression of Point Sampled Surfaces
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley and Sons Ltd.
Abstract
Most surfaces, be it from a fine-art artifact or a mechanical object, are characterized by a strong self-similarity. This property finds its source in the natural structures of objects but also in the fabrication processes: regularity of the sculpting technique, or machine tool. In this paper, we propose to exploit the self-similarity of the underlying shapes for compressing point cloud surfaces which can contain millions of points at a very high precision. Our approach locally resamples the point cloud in order to highlight the self-similarity of the shape, while remaining consistent with the original shape and the scanner precision. It then uses this self-similarity to create an ad hoc dictionary on which the local neighborhoods will be sparsely represented, thus allowing for a light-weight representation of the total surface. We demonstrate the validity of our approach on several point clouds from finearts and mechanical objects, as well as a urban scene. In addition, we show that our approach also achieves a filtering of noise whose magnitude is smaller than the scanner precision.
Description
@article{:10.1111/cgf.12305,
journal = {Computer Graphics Forum},
title = {{Self-similarity for Accurate Compression of Point Sampled Surfaces}},
author = {Digne, Julie and Chaine, Raphaëlle and Valette, Sébastien},
year = {2014},
publisher = {The Eurographics Association and John Wiley and Sons Ltd.},
ISSN = {1467-8659},
DOI = {/10.1111/cgf.12305}
}