DOI: 10.5281/zenodo.495570
title: Cad 120 Affordance Dataset

description: % ==============================================================================<br>
% CAD 120 Affordance Dataset<br>
% Version 1.0<br>
% ------------------------------------------------------------------------------<br>
% If you use the dataset please cite:<br>
%<br>
% Johann Sawatzky, Abhilash Srikantha, Juergen Gall.<br>
% Weakly Supervised Affordance Detection.<br>
% IEEE Conference on Computer Vision and Pattern Recognition (CVPR'17)<br>
%<br>
% and<br>
%<br>
% H. S. Koppula and A. Saxena.<br>
% Physically grounded spatio-temporal object affordances.<br>
% European Conference on Computer Vision (ECCV'14)<br>
%<br>
% Any bugs or questions, please email sawatzky AT iai DOT uni-bonn DOT de.<br>
% ==============================================================================

This is the CAD 120 Affordance Segmentation Dataset based on the Cornell Activity<br>
Dataset CAD 120 (see http://pr.cs.cornell.edu/humanactivities/data.php).

Content

frames/*.png:<br>
RGB frames selected from Cornell Activity Dataset. To find out the location of the frame<br>
in the original videos, see video_info.txt.

object_crop_images/*.png<br>
image crops taken from the selected frames and resized to 321*321. Each crop is a padded<br>
bounding box of an object the human interacts with in the video. Due to the padding,<br>
the crops may contain background and other objects.<br>
In each selected frame, each bounding box was processed. The bounding boxes are already<br>
given in the Cornell Activity Dataset.<br>
The 5-digit number gives the frame number, the second number gives the bounding box number<br>
within the frame.

segmentation_mat/*.mat<br>
321*321*6 segmentation masks for the image crops. Each channel corresponds to an<br>
affordance (openabe, cuttable, pourable, containable, supportable, holdable, in this order).<br>
All pixels belonging to a particular affordance are labeled 1 in the respective channel,<br>
otherwise 0.  

segmentation_png/*.png<br>
321*321 png images, each containing the binary mask for one of the affordances.

lists/*.txt<br>
Lists containing the train and test sets for two splits. The actor split ensures that<br>
train and test images stem from different videos with different actors while the object split ensures<br>
that train and test data have no (central) object classes in common.<br>
The train sets are additionally subdivided into 3 subsets A,B and C. For the actor split,<br>
the subsets stem from different videos. For the object split, each subset contains<br>
every third crop of the train set.

crop_coordinate_info.txt<br>
Maps image crops to their coordinates in the frames.

hpose_info.txt<br>
Maps frames to 2d human pose coordinates. Hand annotated by us.

object_info.txt<br>
Maps image crops to the (central) object it contains.

visible_affordance_info.txt<br>
Maps image crops to affordances visible in this crop

 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55<br>
The crops contain the following object classes:<br>
1.table<br>
2.kettle<br>
3.plate<br>
4.bottle<br>
5.thermal cup<br>
6.knife<br>
7.medicine box<br>
8.can<br>
9.microwave<br>
10.paper box<br>
11.bowl<br>
12.mug

Affordances in our set:<br>
1.openable<br>
2.cuttable<br>
3.pourable<br>
4.containable<br>
5.supportable<br>
6.holdable

Note that our object affordance labeling differs from the Cornell Activity Dataset:<br>
E.g. the cap of a pizza box is considered to be supportable.
description: Acknowledgments. The work has been financially sup-
ported by the DFG projects GA 1927/5-1 (DFG Research
Unit FOR 2535 Anticipating Human Behavior) and GA
1927/2-2 (DFG Research Unit FOR 1505 Mapping on De-
mand).
description: {"references": ["Sawatzky, J., Srikantha, A., Gall, J.: Weakly supervised affordance detection.  CVPR (2017)"]}