Browsing by Author "Tortorici, Claudio"

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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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    A Sparse Mesh Sampling Scheme for Graph-based Relief Pattern Classification
    (The Eurographics Association, 2023) Paolini, Gabriele; Guiducci, Niccolò; Tortorici, Claudio; Berretti, Stefano; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, Gilda
    In the context of geometric deep learning, the classification of relief patterns involves recognizing the surface characteristics of a 3D object, regardless of its global shape. State-of-the-art methods leverage powerful 2D deep learning image-based techniques by converting local patches of the surface into a texture image. However, their effectiveness is guaranteed only when the mesh is simple enough to allow this projection onto a 2D subspace. Therefore, developing deep learning techniques that can work directly on manifolds represents an interesting line of research for addressing these challenges. The objective of our paper is to extend and enhance the architecture described in a recent GNN approach for a relief pattern classifier through the introduction of a new sampling tecnhique for meshes. In their method, local mesh structures, referred to as SpiderPatches, are connected to form the nodes of a graph, called MeshGraph, that captures global structures of the mesh. These two data structures are then fed into a bi-level architecture based on Graph Attention Networks. The MeshGraph construction proves important in ensuring optimal classification results. By the proposed subsampling process, we tackle the problem of fine-tuning multiple hyperparameters inherent the MeshGraph by defining a graph structure that is aware of the mesh geometric details. We demonstrate that the graph constructed using this approach robustly captures the relief patterns on the surface, obviating the need for data augmentation during training. The resulting network is robust, easily customizable, and shows comparable performance to recent methods, all while operating directly on 3D data.