3DOR: Eurographics Workshop on 3D Object Retrieval

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Browsing 3DOR: Eurographics Workshop on 3D Object Retrieval by Subject "and systems"

Now showing 1 - 4 of 4
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    3D GrabCut: Interactive Foreground Extraction for Reconstructed 3D Scenes
    (The Eurographics Association, 2015) Meyer, Gregory P.; Do, Minh N.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    In the near future, mobile devices will be able to measure the 3D geometry of an environment using integrated depth sensing technology. This technology will enable anyone to reconstruct a 3D model of their surroundings. Similar to natural 2D images, a 3D model of a natural scene will occasionally contain a desired foreground object and an unwanted background region. Inspired by GrabCut for still images, we propose a system to perform interactive foreground/background segmentation on a reconstructed 3D scene using an intuitive user interface. Our system is designed to enable anyone, regardless of skill, to extract a 3D object from a 3D scene with a minimal amount of effort. The only input required by the user is a rectangular box around the desired object. We performed several experiments to demonstrate that our system produces high-quality segmentation on a wide variety of 3D scenes.
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    Deformable Shape Retrieval with Missing Parts
    (The Eurographics Association, 2017) RodolĂ , E.; Cosmo, L.; Litany, O.; Bronstein, M. M.; Bronstein, A. M.; Audebert, N.; Hamza, A. Ben; Boulch, A.; Castellani, U.; Do, M. N.; Duong, A.-D.; Furuya, T.; Gasparetto, A.; Hong, Y.; Kim, J.; Saux, B. Le; Litman, R.; Masoumi, M.; Minello, G.; Nguyen, H.-D.; Nguyen, V.-T.; Ohbuchi, R.; Pham, V.-K.; Phan, T. V.; Rezaei, M.; Torsello, A.; Tran, M.-T.; Tran, Q.-T.; Truong, B.; Wan, L.; Zou, C.; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
    Partial similarity problems arise in numerous applications that involve real data acquisition by 3D sensors, inevitably leading to missing parts due to occlusions and partial views. In this setting, the shapes to be retrieved may undergo a variety of transformations simultaneously, such as non-rigid deformations (changes in pose), topological noise, and missing parts - a combination of nuisance factors that renders the retrieval process extremely challenging. With this benchmark, we aim to evaluate the state of the art in deformable shape retrieval under such kind of transformations. The benchmark is organized in two sub-challenges exemplifying different data modalities (3D vs. 2.5D). A total of 15 retrieval algorithms were evaluated in the contest; this paper presents the details of the dataset, and shows thorough comparisons among all competing methods.
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    Shape Retrieval of Low-Cost RGB-D Captures
    (The Eurographics Association, 2016) Pascoal, Pedro B.; Proença, Pedro; Gaspar, Filipe; Dias, Miguel Sales; Ferreira, Alfredo; Tatsuma, Atsushi; Aono, Masaki; Logoglu, K. Berker; Kalkan, Sinan; Temizel, Alptekin; Li, Bo; Johan, Henry; Lu, Yijuan; Seib, Viktor; Link, Norman; Paulus, Dietrich; A. Ferreira and A. Giachetti and D. Giorgi
    RGB-D cameras allow to capture digital representations of objects in an easy and inexpensive way. Such technology enables ordinary users to capture everyday object into digital 3D representations. In this context, we present a track for the Shape Retrieval Contest, which focus on objects digitized using the latest version of Microsoft Kinect, namely, Kinect One. The proposed, track encompasses a dataset of two hundred objects and respective classification.
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    SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras
    (The Eurographics Association, 2013) Machado, J.; Ferreira, A.; Pascoal, P. B.; Abdelrahman, M.; Aono, M.; El-Melegy, M.; Farag, A.; Johan, H.; Li, B.; Lu, Y.; Tatsuma, A.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set.