Browsing by Author "Villanueva, Alberto Jaspe"

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    Automatic Modeling of Cluttered Multi-room Floor Plans From Panoramic Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Pintore, Giovanni; Ganovelli, Fabio; Villanueva, Alberto Jaspe; Gobbetti, Enrico; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    We present a novel and light-weight approach to capture and reconstruct structured 3D models of multi-room floor plans. Starting from a small set of registered panoramic images, we automatically generate a 3D layout of the rooms and of all the main objects inside. Such a 3D layout is directly suitable for use in a number of real-world applications, such as guidance, location, routing, or content creation for security and energy management. Our novel pipeline introduces several contributions to indoor reconstruction from purely visual data. In particular, we automatically partition panoramic images in a connectivity graph, according to the visual layout of the rooms, and exploit this graph to support object recovery and rooms boundaries extraction. Moreover, we introduce a plane-sweeping approach to jointly reason about the content of multiple images and solve the problem of object inference in a top-down 2D domain. Finally, we combine these methods in a fully automated pipeline for creating a structured 3D model of a multi-room floor plan and of the location and extent of clutter objects. These contribution make our pipeline able to handle cluttered scenes with complex geometry that are challenging to existing techniques. The effectiveness and performance of our approach is evaluated on both real-world and synthetic models.
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    Crack Detection in Single- and Multi-Light Images of Painted Surfaces using Convolutional Neural Networks
    (The Eurographics Association, 2019) Dulecha, Tinsae Gebrechristos; Giachetti, Andrea; Pintus, Ruggero; Ciortan, Irina; Villanueva, Alberto Jaspe; Gobbetti, Enrico; Rizvic, Selma and Rodriguez Echavarria, Karina
    Cracks represent an imminent danger for painted surfaces that needs to be alerted before degenerating into more severe aging effects, such as color loss. Automatic detection of cracks from painted surfaces' images would be therefore extremely useful for art conservators; however, classical image processing solutions are not effective to detect them, distinguish them from other lines or surface characteristics. A possible solution to improve the quality of crack detection exploits Multi-Light Image Collections (MLIC), that are often acquired in the Cultural Heritage domain thanks to the diffusion of the Reflectance Transformation Imaging (RTI) technique, allowing a low cost and rich digitization of artworks' surfaces. In this paper, we propose a pipeline for the detection of crack on egg-tempera paintings from multi-light image acquisitions and that can be used as well on single images. The method is based on single or multi-light edge detection and on a custom Convolutional Neural Network able to classify image patches around edge points as crack or non-crack, trained on RTI data. The pipeline is able to classify regions with cracks with good accuracy when applied on MLIC. Used on single images, it can give still reasonable results. The analysis of the performances for different lighting directions also reveals optimal lighting directions.
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    Web-based Multi-layered Exploration of Annotated Image-based Shape and Material Models
    (The Eurographics Association, 2019) Villanueva, Alberto Jaspe; Pintus, Ruggero; Giachetti, Andrea; Gobbetti, Enrico; Rizvic, Selma and Rodriguez Echavarria, Karina
    We introduce a novel versatile approach for letting users explore detailed image-based shape and material models integrated with structured, spatially-associated descriptive information. We represent the objects of interest as a series of registered layers of image-based shape and material information. These layers are represented at multiple scales, and can come out of a variety of pipelines and include both RTI representations and spatially-varying normal and BRDF fields, eventually as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer can be easily authored at data preparation time using widely available image editing tools. At run-time, an annotated multi-layered dataset is made available to clients by a standard web server. Users can explore these datasets on a variety of devices, from mobile phones to large scale displays in museum installations, using JavaScript/WebGL2 clients capable to perform layer selection, interactive relighting and enhanced visualization, annotation display, and focus-and-context multiple-layer exploration using a lens metaphor. The capabilities of our approach are demonstrated on a variety of cultural heritage use cases involving different kinds of annotated surface and material models.