A comparative study of existing metrics for 3D-mesh segmentation evaluation
Résumé
In this paper, we present an extensive experimental comparison of existing similarity metrics addressing the quality assessment problem of mesh seg- mentation. We introduce a new metric named the 3D Normalized Probabilistic Rand Index (3D-NPRI) which outperforms the others in terms of properties and discriminative power. This comparative study includes a subjective experiment with human observers and is based on a corpus of manually segmented models. This corpus is an improved version of our previous one. It is composed of a set of 3D-mesh models grouped in different classes associated with several manual ground-truth segmentations. Finally the 3D-NPRI is applied to evaluate six recent segmentation algorithms using our corpus and the Chen's et al. corpus.
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