• DocumentCode
    128720
  • Title

    Benchmark datasets for 3D computer vision

  • Author

    Yulan Guo ; Jun Zhang ; Min Lu ; Jianwei Wan ; Yanxin Ma

  • Author_Institution
    Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2014
  • fDate
    9-11 June 2014
  • Firstpage
    1846
  • Lastpage
    1851
  • Abstract
    With the rapid development of range image acquisition techniques, 3D computer vision has became a popular research area. It has numerous applications in various domains including robotics, biometrics, remote sensing, entertainment, civil construction, and medical treatment. Recently, a large number of algorithms have been proposed to address specific problems in the area of 3D computer vision. Meanwhile, several benchmark datasets have also been released to stimulate the research in this area. The availability of benchmark datasets plays an significant role in the process of technological progress. In this paper, we first introduce several major 3D acquisition techniques. We also present an overview on various popular topics in 3D computer vision including 3D object modeling, 3D model retrieval, 3D object recognition, 3D face recognition, RGB-D vision, and 3D remote sensing. Moreover, we present a contemporary summary of the existing benchmark datasets in 3D computer vision. This paper can therefore, serve as a handbook for those who are working in the related areas.
  • Keywords
    computer vision; face recognition; image retrieval; object recognition; 3D acquisition techniques; 3D computer vision; 3D face recognition; 3D model retrieval; 3D object modeling; 3D object recognition; 3D remote sensing; RGB-D vision; benchmark datasets; biometrics; civil construction; entertainment; medical treatment; range image acquisition technique; robotics; Benchmark testing; Face; Face recognition; Mathematical model; Shape; Solid modeling; Three-dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-4316-6
  • Type

    conf

  • DOI
    10.1109/ICIEA.2014.6931468
  • Filename
    6931468