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Self‐similarity for accurate compression of point sampled surfaces
Author(s) -
Digne Julie,
Chaine Raphaëlle,
Valette Sébastien
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12305
Subject(s) - point cloud , computer science , similarity (geometry) , scanner , artificial intelligence , computer vision , representation (politics) , property (philosophy) , point (geometry) , object (grammar) , exploit , self similarity , pattern recognition (psychology) , computer graphics (images) , image (mathematics) , mathematics , geometry , philosophy , epistemology , politics , political science , law , computer security
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 fine‐arts 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.