3DOR 2020 - Short Papers

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https://diglib.eg.org/handle/10.2312/2632930

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Browsing 3DOR 2020 - Short Papers by Subject "Computing methodologies"

Now showing 1 - 3 of 3
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    Fast Feature Curve Extraction for Similarity Estimation of 3D Meshes
    (The Eurographics Association, 2020) Romanelis, Ioannis; Arvanitis, Gerasimos; Moustakas, Konstantinos; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Feature extraction from 3D triangle meshes is a very popular and important task that could contribute to many scientific fields such as computer vision, pattern recognition, medical 3D modeling, etc. However, the main challenge is not just finding corners and edges of 3D models but to automatically extract connected clusters of vertices that jointly represent a feature curve. This paper presents an approach for feature curve extraction and similarity evaluation among feature curves of the same or other models robust to differences in scale, resolution quality, pose, or partial observation. The proposed approach could be used, as a pre-processing step, in many other applications like registration, partial matching, tracking, object recognition, etc. Extensive evaluation studies and experiments carried out using a variety of different models and use cases, verify that the proposed approach achieves accurate feature curve extraction and categorization, robust to several constraints like scale or resolution.
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    SHREC 2020 Track: Non-rigid Shape Correspondence of Physically-Based Deformations
    (The Eurographics Association, 2020) Dyke, Roberto M.; Zhou, Feng; Lai, Yu-Kun; Rosin, Paul L.; Guo, Daoliang; Li, Kun; Marin, Riccardo; Yang, Jingyu; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Commonly, novel non-rigid shape correspondence techniques focus on particular matching challenges. This can lead to the potential trade-off of poorer performance in other scenarios. An ideal dataset would provide a granular means for degrees of evaluation. In this paper, we propose a novel dataset of real scans that contain challenging non-isometric deformations to evaluate non-rigid point-to-point correspondence and registration algorithms. The deformations included in our dataset cover extreme types of physically-based contortions of a toy rabbit. Furthermore, shape pairs contain incrementally different types and amounts of deformation, this enables performance to be systematically evaluated with respect to the nature of the deformation. A brief investigation into different methods for initialising correspondence was undertaken, and a series of experiments were subsequently conducted to investigate the performance of state-of-the-art methods on the proposed dataset. We find that methods that rely on initial correspondences and local descriptors that are sensitive to local surface changes perform poorly in comparison to other strategies, and that a template-based approach performs the best.
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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.