Feb 8, 2016

Indoor scene dataset collection


  • Berkeley B3DO
    • 849 images.
    • 50 categories.
    • Bounding box level annotation.
  • SUN 3D
    • polygonal 2D semantic annotation of total of 10335 RGBD images. (additional 3D polygon for 3D detection is also available, 3D polygonal for room layout annotation.)
    • 4943 new images captured.
      • 3784 images using (Kinect v2).
      • 1159 using (Intel Realsense ).
    • 1449 from NYUD v2 (Kinect v1).
    • 554 from B3DO (Kinect v1).
    • 3389 selected frames from SUN3D videos (Asus Xtion).
    • 19 scene-types (includes most of the NYUD v2 scenes).
    •  Reference: semantic segmentation different CNN models
  • Robot in a room 
    • Not available.
  • NYUD v1
    • 7 scenes-types with full labels.
    • 12 object classes to classify.
    • total 2347 images.
  • NYUD v2 
    • 9 dominant scenes-types.
    • 37 dominant categories of object classes.
    • total 1449 images.
  • Cornell RGBD 
    • Office and home scene-types
    • 28 office scenes (each scene has point cloud annotated bag file)
    • 24 home scenes
    • separate image with label for each frame is available.
  • COLD (Ljubljana+Freiburg+Strucken) 
    • 5 common scenes (bathroom, kitchen, printer, office, hallway) in all three geo-locations. Additionally other scenes as sequence images.
    • RGB only
  • karpathy Discovery 
    • 58 scenes (like Cornell RGBD) of office+kitchen+printer
      • 36 office desks + 7 bookshelves + 4 printers + 3 kitchen coutners + 8 miscellaneous living space scenes.
    • Types of objects are different than that of NYUD v2; more object centric.
    • 3D meshes representations of the scene from kinect fusion + post-processing.
  • paspart_challenge: part segmentation 
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