Self-similarity for Accurate Compression of Point Sampled Surfaces
| dc.contributor.author | Digne, Julie | en_US |
| dc.contributor.author | Chaine, Raphaëlle | en_US |
| dc.contributor.author | Valette, Sébastien | en_US |
| dc.contributor.editor | B. Levy and J. Kautz | en_US |
| dc.date.accessioned | 2015-03-03T12:27:23Z | |
| dc.date.available | 2015-03-03T12:27:23Z | |
| dc.date.issued | 2014 | en_US |
| dc.description.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. | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.identifier.doi | 10.1111/cgf.12305 | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12305 | en_US |
| dc.publisher | The Eurographics Association and John Wiley and Sons Ltd. | en_US |
| dc.title | Self-similarity for Accurate Compression of Point Sampled Surfaces | en_US |