Self-similarity for accurate compression of point sampled surfaces

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Julie Digne   Raphaëlle Chaine   Sébastien Valette

An input point cloud, its seeds and its decompression
(Initial point cloud: 15Mio points; compressed file size 1.15MB; 0.59bit per point)

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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 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.

An input point cloud and its decompression
(Initial point cloud: 10Mio points; compressed file size 1.2MB; 0.96bit per point)

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