Browsing by Author "Biasotti, Silvia"

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    3DOR 2019: Frontmatter
    (Eurographics Association, 2019) Biasotti, Silvia; Lavoué, Guillaume; Veltkamp, Remco; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
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    SHREC 2020 Track: River Gravel Characterization
    (The Eurographics Association, 2020) Giachetti, Andrea; Biasotti, Silvia; Moscoso Thompson, Elia; Fraccarollo, Luigi; Nguyen, Quang; Nguyen, Hai-Dang; Tran, Minh-Triet; Arvanitis, Gerasimos; Romanelis, Ioannis; Fotis, Vlasis; Moustakas, Konstantinos; Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    The quantitative analysis of the distribution of the different types of sands, gravels and cobbles shaping river beds is a very important task performed by hydrologists to derive useful information on fluvial dynamics and related processes (e.g., hydraulic resistance, sediment transport and erosion, habitat suitability. As the methods currently employed in the practice to perform this evaluation are expensive and time-consuming, the development of fast and accurate methods able to provide a reasonable estimate of the gravel distribution based on images or 3D scanning data would be extremely useful to support hydrologists in their work. To evaluate the suitability of state-of-the-art geometry processing tool to estimate the distribution from digital surface data, we created, therefore, a dataset including real captures of riverbed mockups, designed a retrieval task on it and proposed them as a challenge of the 3D Shape Retrieval Contest (SHREC) 2020. In this paper, we discuss the results obtained by the methods proposed by the groups participating in the contest and baseline methods provided by the organizers. Retrieval methods have been compared using the precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall. Results show the feasibility of gravels characterization from captured surfaces and issues in the discrimination of mixture of gravels of different size.
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    SHREC 2023: Detection of Symmetries on 3D Point Clouds Representing Simple Shapes
    (The Eurographics Association, 2023) Sipiran, Ivan; Romanengo, Chiara; Falcidieno, Bianca; Biasotti, Silvia; Arvanitis, Gerasimos; Chen, Chen; Fotis, Vlassis; He, Jianfang; Lv, Xiaoling; Moustakas, Konstantinos; Peng, Silong; Romanelis, Ioannis; Sun, Wenhao; Vlachos, Christoforos; Wu, Ziyu; Xie, Qiong; Fugacci, Ulderico; Lavoué, Guillaume; Veltkamp, Remco C.
    This paper presents the methods that participated in the SHREC 2023 track focused on detecting symmetries on 3D point clouds representing simple shapes. By simple shapes, we mean surfaces generated by different types of closed plane curves used as the directrix of a cylinder or a cone. This track aims to determine the reflective planes for each point cloud. The methods are evaluated in their capability of detecting the right number of symmetries and correctly identifying the reflective planes. To this end, we generated a dataset that contains point clouds representing simple shapes perturbed with different kinds of artefacts (such as noise and undersampling) to provide a thorough evaluation of the robustness of the algorithms.
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    Towards an Automatic 3D Patterns Classification: the GRAVITATE Use Case
    (The Eurographics Association, 2018) Thompson, Elia Moscoso; Biasotti, Silvia; Sorrentino, Giusi; Polig, Martina; Hermon, Sorin; Sablatnig, Robert and Wimmer, Michael
    When cataloging archaeological fragments, decorative patterns are an indicator of the stylistic canon an object belongs to. In this paper we address a quantitative classification of the decorative pattern elements that characterize the models in the GRAVITATE use case, discussing the performance of a recent algorithm for pattern recognition over triangle meshes.