Abstract
Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching.
Highlights
In last years, a considerable number of 3D models is growing in digital form on the World Wide Web, and this amount intends to increase in the future
We propose to use a similarity search technique that compares the similarity between portions of 3D models rather than a global comparison
We propose a new approach based on partial matching technique in which each 3D object is segmented into its different segments, and shape descriptors are computed from these elements to compare similarities
Summary
A considerable number of 3D models is growing in digital form on the World Wide Web, and this amount intends to increase in the future. Several techniques of 3D shape analysis and matching have been developed; among them shape similarity, indexation, shape retrieval, mesh segmentation, and many others. The content-based retrieval process is done with offline and online steps, which are indexing, querying, matching and visualizing. We can distinguish two types of 3D indexation methods; the global ones that aim to compute a signature for the entire object and the partial methods which consist on matching subparts or regions by calculating the signature of each part of the object and require a segmentation preprocessing step. Despite the variety of research toward indexation, most existing methods for shape analysis are based on global shape similarity functions. Only a very few methods can handle efficiently 3D partial shape retrieval because it is much more difficult than the global similarity search problem, since it has to search and define the subparts before measuring similarities
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