Italian Chapter Conference 2022 - Smart Tools and Apps in Graphics

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https://diglib.eg.org/handle/10.2312/2633222

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Browsing Italian Chapter Conference 2022 - Smart Tools and Apps in Graphics by Subject "Applied computing"

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    MUSE: Modeling Uncertainty as a Support for Environment
    (The Eurographics Association, 2022) Miola, Marianna; Cabiddu, Daniela; Pittaluga, Simone; Vetuschi Zuccolini, Marino; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo
    To fully understand a Natural System, the representation of an environmental variable's distribution in 3D space is a mandatory and complex task. The challenge derives from a scarcity of samples number in the survey domain (e.g., logs in a reservoir, soil samples, fixed acquisition sampling stations) or an implicit difficulty in the in-situ measurement of parameters. Field or lab measurements are generally considered error-free, although not so. That aspect, combined with conceptual and numerical approximations used to model phenomena, makes the results intrinsically less performing, fading the interpretation. In this context, we design a computational infrastructure to evaluate spatial uncertainty in a multi-scenario application in Environment survey and protection, such as in environmental geochemistry, coastal oceanography, or infrastructure engineering. Our Research aims to expand the operative knowledge by developing an open-source stochastic tool, named MUSE, the acronym for Modeling Uncertainty as a Support for Environment. At this stage, the methodology mainly includes the definition of a flexible environmental data format, a geometry processing module to discretize the space, and geostatistics tools to evaluate the spatial continuity of sampled parameters, predicting random variable distribution. The implementation of the uncertainty module and the development of a graphic interface for ad-hoc visualization will be integrated as the next step. The poster summarizes research purposes, and MUSE computational code structure developed so far.
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    SPIDER: SPherical Indoor DEpth Renderer
    (The Eurographics Association, 2022) Tukur, Muhammad; Pintore, Giovanni; Gobbetti, Enrico; Schneider, Jens; Agus, Marco; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo
    Today's Extended Reality (XR) applications that call for specific Diminished Reality (DR) strategies to hide specific classes of objects are increasingly using 360? cameras, which can capture entire areas in a single picture. In this work, we present an interactive-based image editing and rendering system named SPIDER, that takes a spherical 360? indoor scene as input. The system incorporates the output of deep learning models to abstract the segmentation and depth images of full and empty rooms to allow users to perform interactive exploration and basic editing operations on the reconstructed indoor scene, namely: i) rendering of the scene in various modalities (point cloud, polygonal, wireframe) ii) refurnishing (transferring portions of rooms) iii) deferred shading through the usage of precomputed normal maps. These kinds of scene editing and manipulations can be used for assessing the inference from deep learning models and enable several Mixed Reality (XR) applications in areas such as furniture retails, interior designs, and real estates. Moreover, it can also be useful in data augmentation, arts, designs, and paintings.