Weighted Feature Graph via Hierarchical Clustering
| dc.contributor.author | Ladeuil, Mathieu | en_US |
| dc.contributor.author | Trabucato, Marc | en_US |
| dc.contributor.author | Vaisse, Alexis | en_US |
| dc.contributor.author | Faraj, Noura | en_US |
| dc.contributor.editor | Günther, Tobias | en_US |
| dc.contributor.editor | Montazeri, Zahra | en_US |
| dc.date.accessioned | 2025-05-09T09:31:42Z | |
| dc.date.available | 2025-05-09T09:31:42Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | In computer graphics, mesh clustering is a key component of various applications such as shape matching or skinning weight computation, especially when using hierarchical clustering. Garland et al. [GWH01] proposed to build a hierarchy of clusters by simplifying the dual graph of the mesh. We extend their method to provide control over cluster shapes through a combination of error metrics. Additionally, we alleviate the challenging task of finding an optimal threshold (stopping criterion) by considering a weighted feature graph that incorporates persistent cluster information throughout the hierarchy. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Eurographics 2025 - Posters | |
| dc.identifier.doi | 10.2312/egp.20251025 | |
| dc.identifier.isbn | 978-3-03868-269-1 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 2 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egp.20251025 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egp20251025 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Weighted Feature Graph via Hierarchical Clustering | en_US |