SHREC'12 track: 3D mesh segmentation

Authors:
G. Lavoué;J-P. Vandeborre;H. Benhabiles;M. Daoudi;K. Huebner;M. Mortara;M. Spagnuolo
Affiliations:
Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR, France;LIFL, UMR Lille1, CNRS, University of Lille 1, France;Le2i, UMR, CNRS, Université de Bourgogne, France;LIFL, UMR Lille1, CNRS, University of Lille 1, France;Computational Vision & Active Perception Lab, Royal Institute of Technology (KTH), Stockholm, Sweden;CNR-IMATI Genova, Italy;CNR-IMATI Genova, Italy
Venue:
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
Year:
2012

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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.