Browsing by Author "Bettio, Fabio"

Now showing 1 - 3 of 3
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    Ebb & Flow: Uncovering Costantino Nivola's Olivetti Sandcast through 3D Fabrication and Virtual Exploration
    (The Eurographics Association, 2022) Ahsan, Moonisa; Altea, Giuliana; Bettio, Fabio; Callieri, Marco; Camarda, Antonella; Cignoni, Paolo; Gobbetti, Enrico; Ledda, Paolo; Lutzu, Alessandro; Marton, Fabio; Mignemi, Giuseppe; Ponchio, Federico; Ponchio, Federico; Pintus, Ruggero
    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.
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    Effective Interactive Visualization of Neural Relightable Images in a Web-based Multi-layered Framework
    (The Eurographics Association, 2023) Righetto, Leonardo; Bettio, Fabio; Ponchio, Federico; Giachetti, Andrea; Gobbetti, Enrico; Bucciero, Alberto; Fanini, Bruno; Graf, Holger; Pescarin, Sofia; Rizvic, Selma
    Relightable images created from Multi-Light Image Collections (MLICs) are one of the most commonly employed models for interactive object exploration in cultural heritage. In recent years, neural representations have been shown to produce higherquality images, at similar storage costs, with respect to the more classic analytical models such as Polynomial Texture Maps (PTM) or Hemispherical Harmonics (HSH). However, their integration in practical interactive tools has so far been limited due to the higher evaluation cost, making it difficult to employ them for interactive inspection of large images, and to the difficulty in integration cost, due to the need to incorporate deep-learning libraries in relightable renderers. In this paper, we illustrate how a state-of-the-art neural reflectance model can be directly evaluated, using common WebGL shader features, inside a multiplatform renderer. We then show how this solution can be embedded in a scalable framework capable to handle multi-layered relightable models in web settings. We finally show the performance and capabilities of the method on cultural heritage objects.
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    A Novel Approach for Exploring Annotated Data With Interactive Lenses
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Bettio, Fabio; Ahsan, Moonisa; Marton, Fabio; Gobbetti, Enrico; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    We introduce a novel approach for assisting users in exploring 2D data representations with an interactive lens. Focus-andcontext exploration is supported by translating user actions to the joint adjustments in camera and lens parameters that ensure a good placement and sizing of the lens within the view. This general approach, implemented using standard device mappings, overcomes the limitations of current solutions, which force users to continuously switch from lens positioning and scaling to view panning and zooming. Navigation is further assisted by exploiting data annotations. In addition to traditional visual markups and information links, we associate to each annotation a lens configuration that highlights the region of interest. During interaction, an assisting controller determines the next best lens in the database based on the current view and lens parameters and the navigation history. Then, the controller interactively guides the user's lens towards the selected target and displays its annotation markup. As only one annotation markup is displayed at a time, clutter is reduced. Moreover, in addition to guidance, the navigation can also be automated to create a tour through the data. While our methods are generally applicable to general 2D visualization, we have implemented them for the exploration of stratigraphic relightable models. The capabilities of our approach are demonstrated in cultural heritage use cases. A user study has been performed in order to validate our approach.