Browsing by Author "Ganovelli, Fabio"

Now showing 1 - 4 of 4
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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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    Evaluating Deep Learning Methods for Low Resolution Point Cloud Registration in Outdoor Scenarios
    (The Eurographics Association, 2021) Siddique, Arslan; Corsini, Massimiliano; Ganovelli, Fabio; Cignoni, Paolo; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    Point cloud registration is a fundamental task in 3D reconstruction and environment perception. We explore the performance of modern Deep Learning-based registration techniques, in particular Deep Global Registration (DGR) and Learning Multiview Registration (LMVR), on an outdoor real world data consisting of thousands of range maps of a building acquired by a Velodyne LIDAR mounted on a drone. We used these pairwise registration methods in a sequential pipeline to obtain an initial rough registration. The output of this pipeline can be further globally refined. This simple registration pipeline allow us to assess if these modern methods are able to deal with this low quality data. Our experiments demonstrated that, despite some design choices adopted to take into account the peculiarities of the data, more work is required to improve the results of the registration.
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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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    State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Pintore, Giovanni; Mura, Claudio; Ganovelli, Fabio; Fuentes-Perez, Lizeth Joseline; Pajarola, Renato; Gobbetti, Enrico; Mantiuk, Rafal and Sundstedt, Veronica
    Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.