Lavoué, G.Vandeborre, J-P.Benhabiles, H.Daoudi, M.Huebner, K.Mortara, M.Spagnuolo, M.M. Spagnuolo and M. Bronstein and A. Bronstein and A. Ferreira2013-09-242013-09-242012978-3-905674-36-11997-0463https://doi.org/10.2312/3DOR/3DOR12/093-0993D 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.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