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In the last decades, 3D models have known a significant expansion, as we can see them in many fields such as computer vision, biology, augmented reality, engineering, and even medicine. They become more and more popular and accessible than ever. Due to these facts, there is an increasing need for a retrieval system, which can return the most similar 3D objects existing in the database to the query. In the present paper, a new retrieval approach is proposed, based on a multi-criteria method to generate a compact descriptor, which represents a signature for each 3D model. The key idea behind this approach is to try to extract the best out of each criterion (i.e., measure) by extracting a combined score using the Data envelopment analysis method (DEA). The results of the proposed method are very satisfactory and outperform some commonly used retrieval methods which highlight the potential and the performance of our approach. [ABSTRACT FROM AUTHOR]

Decomposing a 3D mesh into significant regions is considered as a fundamental process in computer graphics, since several algorithms use the segmentation results as an initial step, such as, skeleton extraction, shape retrieval, shape correspondence, and compression. In this work, we present a new segmentation algorithm using spectral clustering where the affinity matrix is constructed by combining the minimal curvature and dihedral angles to detect both concave and convex properties of each edge. Experimental results show that the proposed method outperforms some of the existing segmentation methods, which highlight the performance of our approach. [ABSTRACT FROM AUTHOR]

3D mesh segmentation is an important preprocessing in many 3D shape applications and it is considered as one of the most challenging tasks in 3D mesh analysis. Recently, interests have been developed in creating different approaches for 3D mesh segmentation and many solutions have been proposed in the literature. Hence, one of the most important issues is how to evaluate the proposed segmentation methods and judge the quality of segmentation results. In this paper, we present a new evaluation metric, based on the Levenshtein distance to perform an objective evaluation of 3D mesh segmentation methods. Most of the existing metrics judge the quality of an automatic segmentation with only one ground-truth, which is considered as a limit since we can have many semantic segmentations for the 3D meshes. Furthermore, they do not take into consideration the regularity of meshes. Therefore, the novel metric allows both comparison with multiple ground truth segmentations and has the ability to give a relevant evaluation for both regular and irregular 3D meshes. Several experiments have been done to show the performance and the potential of the proposed metric. [ABSTRACT FROM AUTHOR]