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To fully exploit the valuable knowledge embedded in repositories of digital models, it is crucial to devise search engines capable of expressing high-level and advanced queries, which can effectively support the re-use of CAD models. The retrieval mechanism should be able to return not only global similarity measures among objects, but it should be also(More)
The Network of Excellence AIM@SHAPE organized SHREC, the 3D Shape Retrieval Contest for the first time in 2006. The general objective is to evaluate the effectiveness of 3D-shape retrieval algorithms. There was only one track, the retrieval of polygonal models that are not necessarily watertight (polygon soups), and there were eight participants. For more(More)
Assessing the similarity among 3D shapes is a challenging research topic, and effective shape descriptions have to be devised in order to support the matching process. There is a growing consensus that shapes are recognized and coded mentally in terms of relevant parts and their spatial configuration, or structure. The presentation will discuss the(More)
This tutorial covers a variety of methods for 3D shape matching and retrieval that are characterized by the use of a real-valued function defined on the shape (mapping function) to derive its signature. The methods are discussed following an abstract conceptual framework that distinguishes among the three main components of these class of shape matching(More)
Research in content-based 3D retrieval has already started, and several approaches have been proposed which use in different manner a similarity assessment to match the shape of the query against the shape of the objects in the database. However, the success of these solutions are far from the success obtained by their textual counterparts. A major drawback(More)
In the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify a specific set of eigenvalues such that they maximise the retrieval and/or the classification performance on the input benchmark data(More)