Italian Chapter Conference 2020 - Smart Tools and Apps in computer Graphics

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https://diglib.eg.org/handle/10.2312/2632951

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Browsing Italian Chapter Conference 2020 - Smart Tools and Apps in computer Graphics by Subject "based models"

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    Deep-learning Alignment for Handheld 3D Acquisitions: A new Densematch Dataset for an Extended Comparison
    (The Eurographics Association, 2020) Lombardi, Marco; Savardi, Mattia; Signoroni, Alberto; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Promising solutions for the alignment of 3D views based on representation learning approaches have been proposed very recently. The potentials of these solutions that could positively affect the 3D object registration has yet to be extensively tested. In fact, a direct comparison among advisable technologies is still lacking, especially if the focus is on different data types and real-time application requirements. This work is a first contribution in this direction since we perform an independent extended comparison among prominent deep learning-driven 3D view alignment solutions by considering two relevant setups: 1) data coming from commodity 3D sensors, and 2) denser data coming from a handheld 3D optical scanner. While for the first scenario reference datasets exist, we collect and release the new benchmark dataset DenseMatch for the second setup.
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    Digital Terrain Model From UAV Photogrammetric Data
    (The Eurographics Association, 2020) Morel, Jules; Bac, Alexandra; Kanai, Takashi; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    This paper presents a method designed to finely approximate ground surfaces from UAV photogrammetric point clouds by relying on statistical filters to separate vegetation from potential ground points, dividing the whole plot in similar complexity sub-plots through an optimized tilling, and filling holes by blending multiple local approximations through the partition of unity principle. Experiments on very different terrain topology show that our approach leads to significant improvement over the state-of-the-art method.
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    Kernel-Based Sampling of Arbitrary Data
    (The Eurographics Association, 2020) Cammarasana, Simone; Patanè, Giuseppe; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Point sampling is widely used in several Computer Graphics' applications, such as point-based modelling and rendering, image and geometric processing. Starting from the kernel-based sampling of signals defined on a regular grid, which generates adaptive distributions of samples with blue-noise property, we specialise this sampling to arbitrary data in terms of dimension and structure, such as signals, vector fields, curves, and surfaces. To demonstrate the novelties and benefits of the proposed approach, we discuss its applications to the resampling of 2D/3D domains according to the distribution of physical quantities computed as solutions to PDEs, and to the sampling of vector fields, 2D curves and 3D point sets. According to our experiments, the proposed sampling achieves a high approximation accuracy, preserves the features of the input data, and is computationally efficient.