- 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
Feb 8, 2016
Indoor scene dataset collection
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