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International audience; This paper presents the results of the SHREC'19 track: Feature curve extraction on triangle meshes. Given a model, the challenge consists in automatically extracting a subset of the mesh vertices that jointly represent a feature curve. As an optional task, participants were requested to send also a similarity evaluation among the feature curves extracted. The various approaches presented by the participants are discussed, together with their results. The proposed methods highlight different points of view of the problem of feature curve extraction. It is interesting to see that it is possible to deal with this problem with good results, despite the different approaches.

This paper presents the results of the SHREC'19 track: Feature curve extraction on triangle meshes. Given a model, the challenge consists in automatically extracting a subset of the mesh vertices that jointly represent a feature curve. As an optional task, participants were requested to send also a similarity evaluation among the feature curves extracted. The various approaches presented by the participants are discussed, together with their results. The proposed methods highlight different points of view of the problem of feature curve extraction. It is interesting to see that it is possible to deal with this problem with good results, despite the different approaches., Eurographics Workshop on 3D Object Retrieval, SHREC Session 2, 85, 92, E. Moscoso Thompson, G. Arvanitis, K. Moustakas, N. Hoang-Xuan, E. R. Nguyen, M. Tran, T. Lejemble, L. Barthe, N. Mellado, C. Romanengo, S. Biasotti, and B. Falcidieno, CCS Concepts: Information systems --> Content analysis and feature selection; Computing methodologies --> Shape analysis; Search methodologies

A method is proposed to identify and localize semantic features like anatomical characteristics or decorations on digital artefacts or fragments, even if the features are partially damaged or incomplete. This technique is based on a novel generalization of the Hough transform. Its major advantages are the relative robustness to noise and the recognition power also in the case of partial features. Our experiments on digital models of real artefacts show the potential of the method, which can work on both 3D meshes and point clouds.

Nel presente articolo presentiamo il progetto “Learning4All” (L4ALL), che ha l’obiettivo di sviluppare le conoscenze riguardo a come tecnologia e pedagogia influenzino la qualità delle esperienze didattiche. Il nostro approccio è empirico piuttosto che teorico: invece di una ricerca nella letteratura, noi raccogliamo prove empiriche da insegnanti italiani che creano innovazione utilizzando la tecnologia nella scuola. Il risultato concreto del progetto sarà un repository online di centinaia di esperienze, condotte in classi di ogni livello scolastico, nelle quali la tecnologia è stata abilmente combinata con soluzioni pedagogiche allo scopo di generare sostanziali benefici didattici. Specifica enfasi del progetto è sul tema dell’inclusione, vale a dire individuare come le diverse esigenze (di alunni con performance al di sopra o al di sotto della media) sono considerate. Questo articolo descrive l’approccio utilizzato, gli strumenti di indagine sviluppati fino a questo momento, come sono stati usati, e che cosa i dati raccolti finora sembrano indicare.

This book is a guide for researchers and practitioners to the new frontiers of 3D shape analysis and the complex mathematical tools most methods rely on. The target reader includes students, researchers and professionals with an undergraduate mathematics background, who wish to understand the mathematics behind shape analysis. The authors begin with a quick review of basic concepts in geometry, topology, differential geometry, and proceed to advanced notions of algebraic topology, always keeping an eye on the application of the theory, through examples of shape analysis methods such as 3D segmentation, correspondence, and retrieval. A number of research solutions in the field come from advances in pure and applied mathematics, as well as from the re-reading of classical theories and their adaptation to the discrete setting. In a world where disciplines (fortunately) have blurred boundaries, the authors believe that this guide will help to bridge the distance between theory and practice.

n 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 set: the first k eigenvalues, by varying k over the cardinality of the spectrum; the Hill Climbing technique; and the AdaBoost algorithm. In this way, we demonstrate that the information coded by the whole spectrum is unnecessary and we improve the shape matching results using only a set of selected eigenvalues. Finally, we test the efficacy of the selected eigenvalues by coupling shape classification and retrieval.