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    <title>DSpace Community:</title>
    <link>https://dspace.crs4.it/jspui/handle/1138/6</link>
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        <rdf:li rdf:resource="https://dspace.crs4.it/jspui/handle/1138/54" />
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    <dc:date>2026-04-05T13:51:17Z</dc:date>
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  <item rdf:about="https://dspace.crs4.it/jspui/handle/1138/54">
    <title>An integrative view of foveated rendering</title>
    <link>https://dspace.crs4.it/jspui/handle/1138/54</link>
    <description>Title: An integrative view of foveated rendering
Authors: Mohanto, Bipul; Islam, AMB Tariqul; Gobbetti, Enrico; Staadt, Oliver
Abstract: Foveated rendering adapts the image synthesis process to the user's gaze. By exploiting the human visual system's limitations, in particular in terms of reduced acuity in peripheral vision, it strives to deliver high-quality visual experiences at very reduced computational, storage, and transmission costs. Despite the very substantial progress made in the past decades, the solution landscape is still fragmented, and several research problems remain open. In this work, we present an up-to-date integrative view of the domain from the point of view of the rendering methods employed, discussing general characteristics, commonalities, differences, advantages, and limitations. We cover, in particular, techniques based on adaptive resolution, geometric simplification, shading simplification, chromatic degradation, as well spatio-temporal deterioration. Next, we review the main areas where foveated rendering is already in use today. We finally point out relevant research issues and analyze research trends.</description>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://dspace.crs4.it/jspui/handle/1138/53">
    <title>Ebb &amp; Flow: Uncovering Costantino Nivola's Olivetti Sandcast through 3D Fabrication and Virtual Exploration</title>
    <link>https://dspace.crs4.it/jspui/handle/1138/53</link>
    <description>Title: Ebb &amp; Flow: Uncovering Costantino Nivola's Olivetti Sandcast through 3D Fabrication and Virtual Exploration
Authors: Ahsan, Moonisa; ALTEA, Giuliana; Bettio, Fabio; Callieri, Marco; CAMARDA, Antonella; Cignoni, Paolo; Gobbetti, Enrico; Ledda, Paolo; Lutzu, Alessandro; Marton, Fabio; Mignemi, Giuseppe; Ponchio, Federico
Abstract: We report on the outcomes of a large multi-disciplinary project targeting the physical reproduction and virtual documentation and exploration of the Olivetti sandcast, a monumental (over 100m²) semi-abstract frieze by the Italian sculptor Costantino Nivola. After summarizing the goal and motivation of the project, we provide details on the acquisition and processing steps that led to the creation of a 3D digital model. We then discuss the technical details and the challenges that we have faced for the physical fabrication process of a massive physical replica, which was the centerpiece of a recent exhibition. We finally discuss the design and application of an interactive web-based tool for the exploration of an annotated virtual replica. The main components of the tool will be released as open source.</description>
    <dc:date>2022-09-01T00:00:00Z</dc:date>
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  <item rdf:about="https://dspace.crs4.it/jspui/handle/1138/52">
    <title>Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections</title>
    <link>https://dspace.crs4.it/jspui/handle/1138/52</link>
    <description>Title: Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections
Authors: Pintus, Ruggero; Ahsan, Moonisa; Zorcolo, Antonio; Bettio, Fabio; Marton, Fabio; Gobbetti, Enrico
Abstract: We present a practical solution to create a relightable model from small Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking a limited number of images with a different local illumination. By exploiting information from neighboring pixels through a carefully-crafted weighting and regularization scheme, we are able to efficiently infer subtle and visually pleasing per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method has a low memory footprint and is easily parallelizabile. We qualitatively and quantitatively evaluated it on both synthetic and real data in the scope of image-based relighting applications.</description>
    <dc:date>2023-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://dspace.crs4.it/jspui/handle/1138/51">
    <title>Scaling deep learning data management with Cassandra DB</title>
    <link>https://dspace.crs4.it/jspui/handle/1138/51</link>
    <description>Title: Scaling deep learning data management with Cassandra DB
Authors: Versaci, Francesco; Busonera, Giovanni</description>
    <dc:date>2021-01-01T00:00:00Z</dc:date>
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