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Kinect Datasets

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Willow. Vocab 3db. Collection of 33 Kinect (RGB+D) datasets with 6D ground truth (by the CVPR team @ Technische Universitat Munchen) Berkeley 3-D Object Dataset. Microsoft - Action Recognition Dataset - Zicheng Liu. MSR Action Recognition Datasets and Codes HON4D Code and MSRActionPairs Dataset MSRGesture3D (28MB): The dataset was captured by a Kinect device.

Microsoft - Action Recognition Dataset - Zicheng Liu

There are 12 dynamic American Sign Language (ASL) gestures, and 10 people. Each person performs each gesture 2-3 times. There are 336 files in total, each corresponding to a depth sequence. . {7,8,9} ->"ASL_Where"; {10,11,12} ->"ASL_Store"; {13,14,15} ->"ASL_Pig"; {16,17,18} ->"ASL_Past"; {19,20,21}->"ASL_Hungary"; {22.23,24}->"ASL_Green"; {25.26.27}->"ASL_Finish"; {28,29,30}->"ASL_Blue"; {31,32,33}->"ASL_Bathroom"; {34,35,36}->"ASL_Milk"; Each file is a MAT file which can be loaded with 64bit MATLAB.

X=load('sub_depth_01_01'); width = size(x.depth_part,1); height = size(x.depth_part,2); nFrames = size(x.depth_part,3); for(i=1:width) for(j=1:height) for(k=1:nFrames) depthval = x.depth_part(i,j,k); end end end The following two papers reported experiment results on this dataset: The format of the skeleton file is as follows. Part 1 (440 MB) Part 2 (540 MB) ETH - kinect:iros2011kinect – ASL Datasets. You can download the 2.6 GB .tar.bz2 file.

ETH - kinect:iros2011kinect – ASL Datasets

The images above show the 3 different experimental setups. This dataset is the companion of our 2011 IROS paper (full text). This dataset contains 27 ROS bags of point clouds produced by a Kinect based the ground truth obtained from a Vicon pose capture system. These runs cover 3 environments of increasing complexity, with 3 types of motions at 3 different speeds. This dataset can be used with our ICP Mapper to track the pose of the Kinect and to explore parameters of ICP algorithms. This dataset is linked to the following paper: F. TUM - Computer Vision Group - Dataset Download. NYU- Depth V2 « Nathan Silberman. Nathan Silberman, Pushmeet Kohli, Derek Hoiem, Rob Fergus If you use the dataset, please cite the following work: Indoor Segmentation and Support Inference from RGBD Images ECCV 2012 [PDF][Bib] Samples of the RGB image, the raw depth image, and the class labels from the dataset.

NYU- Depth V2 « Nathan Silberman

Kinect@Home. Untitled. Www.cas.kth.se/rgb-d/ RGB-D work and datasets at CAS/KTH Exploiting and modeling local 3D structure for predicting object locations We have constructed our dataset from five different sites in Europe; the Technical University of Vienna (TuV), the University of Birmingham (UB), the Royal Institute of Technology (KTH), the German Center for Artificial Intelligence in Saarbrucken (DFKI) and the University of Ljubljana (UL). Downloading the data set To access the dataset drop me a line at aydemir@kth.se , it will be available for direct download as well, we're just in the process of allocating more space in the webserver for it. 3D mapping with the dataset We have also built a method to construct 3D maps, using the dataset.

Example images from the dataset.