Browsing by Author "Scopigno, Roberto"

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    Developing and Maintaining a Web 3D Viewer for the CH Community: an Evaluation of the 3DHOP Framework
    (The Eurographics Association, 2018) Potenziani, Marco; Callieri, Marco; Scopigno, Roberto; Sablatnig, Robert and Wimmer, Michael
    3DHOP (3D Heritage On-line Presenter) has been released 4 years ago, as an open-source framework for the creation of interactive visualization of 3D content on the web, aimed at the CH field. Transforming a research tool into a software ''product'' usable by the heterogeneous CH community is not a simple task and requires a significant amount of resources plus a specific design. This work presents the evolution of the 3DHOP system, and the complex relationship with its community of users, made of content creators, CH experts and general public. We will discuss the new features introduced, as well as the design and implementation strategy employed to maintain the software and make it usable by developers. We will evaluate the effectiveness of the platform by illustrating some of the applications built with 3DHOP either internally or by external users, as well as by presenting the results of a survey aimed at gathering the opinions and suggestions of the user community.
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    Recovering 3D Indoor Floor Plans by Exploiting Low-cost Spherical Photography
    (The Eurographics Association, 2018) Pintore, Giovanni; Ganovelli, Fabio; Pintus, Ruggero; Scopigno, Roberto; Gobbetti, Enrico; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    We present a novel approach to automatically recover, from a small set of partially overlapping panoramic images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. Our improvements over previous approaches include a new method for geometric context extraction based on a 3D facets representation, which combines color distribution analysis of individual images with sparse multi-view clues, as well as an efficient method to combine the facets from different point-of-view in the same world space, considering the reliability of the facets contribution. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments where most of the other previous approaches fail, such as in presence of hidden corners, large clutter and sloped ceilings, even without involving additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes.
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    A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures
    (The Eurographics Association, 2019) Pavoni, Gaia; Corsini, Massimiliano; Palma, Marco; Scopigno, Roberto; Cignoni, Paolo and Miguel, Eder
    The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network.