The Object Segmentation Database (OSD)

Segmentation of unknown objects from RGBD-images

Segmenting unknown objects from generic scenes is one of the ambitious and elusive goals of computer vision. With the recent introduction of cheap and powerful 3D sensors (such as the Microsoft Kinect or Asus XtionPRO) which deliver a dense point cloud plus color for almost any indoor scene, a renewed interest in 3D methods holds the promise to push the envelope slightly further. The Object Segmentation Database provides RGBD data in several subcategories to enable evaluation of object segmentation approaches. The database contains currently 111 entries, all providing the RGBD image, the color image and ground truth annotation.

Authors

Object Segmentation Database

Trainingsset:
  • Boxes (0-16)
  • Stacked Boxes (17-24)
  • Occluded Objects (25-32)
  • Cylindric Objects (33-44)
Testset:
  • Boxes (0-15)
  • Stacked Boxes (16-23)
  • Occluded Objects (24-30)
  • Cylindric Objects (31-42)
  • Mixed Objects (43-54)
  • Complex Scene (55-65)
Download OSD-0.2.tar.gz or OSD-0.2.zip
Download depth images: OSD-0.2-depth.tar.gz or OSD-0.2-depth.zip
(Last update: 22.03.2013)

Example images from the dataset


Object Segmentation Videos



Object Segmentation Images


Publications

Richtsfeld A., Mörwald, T., Prankl, J., Zillich, M. and Vincze, M.:
Segmentation of Unknown Objects in Indoor Environments.
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2012 (pdf)

 

 

Last updated: Mar. 10th 2012
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