3DOR 15

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https://diglib.eg.org/handle/10.2312/12281

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Browsing 3DOR 15 by Subject "Computational Geometry and Object Modeling"

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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    Computing Local Binary Patterns on Mesh Manifolds for 3D Texture Retrieval
    (The Eurographics Association, 2015) Werghi, Naoufel; Tortorici, Claudio; Berretti, Stefano; Bimbo, Alberto Del; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    In this paper, we present and experiment a novel approach for retrieving 3D geometric texture patterns on 2D mesh-manifolds (i.e., surfaces in the 3D space) using local binary patterns (LBP) constructed on the mesh. The method is based on the recently proposed mesh-LBP framework [WBD15]. Compared to its depth-image counterpart, the mesh-LBP is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); b) does not require normalization; c) can accommodate partial matching. Experiments conducted with public 3D models with geometric texture showcase the superiority of the mesh-LBP descriptors in comparison with competitive methods.
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    Retrieval of Objects Captured with Kinect One Camera
    (The Eurographics Association, 2015) Pascoal, Pedro B.; Proença, Pedro; Gaspar, Filipe; Dias, Miguel Sales; Teixeira, Filipe; Ferreira, Alfredo; Seib, Viktor; Link, Norman; Paulus, Dietrich; Tatsuma, Atsushi; Aono, Masaki; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Low-cost RGB-D sensing technology, such as the Microsoft Kinect, is gaining acceptance in the scientific community and even entering into our homes. This technology enables ordinary users to capture everyday object into digital 3D representations. Considering the image retrieval context, whereas the ability to digitalize photos led to a rapid increase of large collections of images, which in turn raised the need of efficient search and retrieval techniques. We believe the same is happening now for the 3D domain. Therefore, it is essential to identify which 3D shape descriptors, provide better matching and retrieval of such digitalized objects. In this paper, we start by presenting a collection of 3D objects acquired using the latest version of Microsoft Kinect, namely, Kinect One. This dataset, comprising 175 common household objects classified into 18 different classes, was then used for the SHape REtrieval Contest (SHREC). Two groups have submitted their 3D matching techniques, providing the rank list with top 10 results, using the complete set of 175 objects as queries.
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    A Spatio-Temporal Descriptor for Dynamic 3D Facial Expression Retrieval and Recognition
    (The Eurographics Association, 2015) Danelakis, Antonios; Theoharis, Theoharis; Pratikakis, Ioannis; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    The recent availability of dynamic 3D facial scans has spawned research activity in recognition based on such data. However, the problem of facial expression retrieval based on dynamic 3D facial data has hardly been addressed and is the subject of this paper. A novel descriptor is created, capturing the spatio-temporal deformation of the 3D facial mesh sequence. Experiments have been implemented using the standard BU - 4DFE dataset. The obtained retrieval results exceed the state-of-the-art results and the new descriptor is much more frugal in terms of space requirements. Furthermore, a methodology which exploits the retrieval results, in order to achieve unsupervised dynamic 3D facial expression recognition is presented, in order to directly compare the proposed descriptor against the wealth of works in recognition. The aforementioned unsupervised methodology outperforms the supervised dynamic 3D facial expression recognition state-of-the-art techniques in terms of classification accuracy.