3DOR 15

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

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Browsing 3DOR 15 by Subject "Geometric algorithms"

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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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    RETRIEVAL 3D: An On-line Content-Based Retrieval Performance Evaluation Tool
    (The Eurographics Association, 2015) Koutsoudis, Anestis; Ioannakis, George; Pratikakis, Ioannis; Chamzas, Christos; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Performance benchmarking is an absolute necessity when attempting to objectively quantify the performance of content-based retrieval methods. For many years now, a number of plot-based and scalar-based measures in combination with benchmark datasets have already been used in order to provide objective results. In this work, we present the first version of an integrated on-line content-based retrieval evaluation tool, named RETRIEVAL 3D, which can be used in order to quantify the performance of a retrieval method. The current version of the system offers a set of popular performance measures that can be accessed through a dynamic visualisation environment. The user is able to upload retrieval results using different input data structures (e.g. binary ranked lists, floating point ranked lists, dissimilarity matrices and groundtruth data) that are already encountered in the literature including the SHREC competition series. Moreover, the system is able to provide evaluation mechanisms for known within the retrieval research community benchmark datasets. It offers performance measures parameterisation that enables the user to determine specific aspects of the evaluated retrieval method. Performance reports archiving and downloading are some of the system's user-oriented functionalities.
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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.