• DocumentCode
    3019907
  • Title

    A category-level 3-D object dataset: Putting the Kinect to work

  • Author

    Janoch, Allison ; Karayev, Sergey ; Jia, Yangqing ; Barron, Jonathan T. ; Fritz, Mario ; Saenko, Kate ; Darrell, Trevor

  • Author_Institution
    Max-Plank-Inst. for Inf., UC Berkeley, Berkeley, Germany
  • fYear
    2011
  • fDate
    6-13 Nov. 2011
  • Firstpage
    1168
  • Lastpage
    1174
  • Abstract
    Recent proliferation of a cheap but quality depth sensor, the Microsoft Kinect, has brought the need for a challenging category-level 3D object detection dataset to the fore. We review current 3D datasets and find them lacking in variation of scenes, categories, instances, and viewpoints. Here we present our dataset of color and depth image pairs, gathered in real domestic and office environments. It currently includes over 50 classes, with more images added continuously by a crowd-sourced collection effort. We establish baseline performance in a PASCAL VOC-style detection task, and suggest two ways that inferred world size of the object may be used to improve detection. The dataset and annotations can be downloaded at http://www.kinectdata.com.
  • Keywords
    image colour analysis; image sensors; object detection; Microsoft Kinect; category variation; category-level 3D object detection; color image pair; crowd-sourced collection effort; depth image pair; depth sensor; instance variation; scene variation; viewpoint variation; Cameras; Detectors; Keyboards; Mice; Robot sensing systems; Shape; Three dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4673-0062-9
  • Type

    conf

  • DOI
    10.1109/ICCVW.2011.6130382
  • Filename
    6130382