Multi-Scale Point Cloud Analysis
| dc.contributor.author | Lejemble, Thibault | en_US |
| dc.contributor.author | Mura, Claudio | en_US |
| dc.contributor.author | Barthe, Loïc | en_US |
| dc.contributor.author | Mellado, Nicolas | en_US |
| dc.contributor.editor | Fusiello, Andrea and Bimber, Oliver | en_US |
| dc.date.accessioned | 2019-05-05T17:48:02Z | |
| dc.date.available | 2019-05-05T17:48:02Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Surfaces sampled with point clouds often exhibit multi-scale properties due to the high variation between their feature size. Traditional shape analysis techniques usually rely on geometric descriptors able to characterize a point and its close neighborhood at multiple scale using smoothing kernels of varying radii. We propose to add a spatial regularization to these point-wise descriptors in two different ways. The first groups similar points in regions that are structured in a hierarchical graph. The graph is then simplified and processed to extract pertinent regions. The second performs a spatial gradient descent in order to highlight stable parts of the surface. We show two experiments focusing on planar and anisotropic feature areas respectively. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Eurographics 2019 - Posters | |
| dc.identifier.doi | 10.2312/egp.20191047 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 17-18 | |
| dc.identifier.uri | https://doi.org/10.2312/egp.20191047 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egp20191047 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Multi-Scale Point Cloud Analysis | en_US |