| dc.contributor.author | Lavoué, G. | en_US |
| dc.contributor.author | Vandeborre, J-P. | en_US |
| dc.contributor.author | Benhabiles, H. | en_US |
| dc.contributor.author | Daoudi, M. | en_US |
| dc.contributor.author | Huebner, K. | en_US |
| dc.contributor.author | Mortara, M. | en_US |
| dc.contributor.author | Spagnuolo, M. | en_US |
| dc.contributor.editor | M. Spagnuolo and M. Bronstein and A. Bronstein and A. Ferreira | en_US |
| dc.date.accessioned | 2013-09-24T10:53:08Z | |
| dc.date.available | 2013-09-24T10:53:08Z | |
| dc.date.issued | 2012 | en_US |
| dc.identifier.isbn | 978-3-905674-36-1 | en_US |
| dc.identifier.issn | 1997-0463 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/3DOR/3DOR12/093-099 | en_US |
| dc.description.abstract | 3D mesh segmentation is a fundamental process in many applications such as shape retrieval, compression, deformation, etc. The objective of this track is to evaluate the performance of recent segmentation methods using a ground-truth corpus and an accurate similarity metric. The ground-truth corpus is composed of 28 watertight models, grouped in five classes (animal, furniture, hand, human and bust) and each associated with 4 ground-truth segmentations done by human subjects. 3 research groups have participated to this track, the accuracy of their segmentation algorithms have been evaluated and compared with 4 other state-of-the-art methods. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling- I.2.10 [Artificial intelligence]: Vision and Scene Understanding-Shape | en_US |
| dc.title | SHREC'12 Track: 3D Mesh Segmentation | en_US |
| dc.description.seriesinformation | Eurographics Workshop on 3D Object Retrieval | en_US |
| dc.description.sectionheaders | SHREC Session | en_US |
