**************************************************************************************** Data: Image depth and label data are in SUNRGBD.zip image: rgb image depth: depth image to read the depth see the code in SUNRGBDtoolbox/read3dPoints/. extrinsics: the rotation matrix to align the point could with gravity fullres: full resolution depth and rgb image intrinsics.txt : sensor intrinsic scene.txt : scene type annotation2Dfinal : 2D segmentation annotation3Dfinal : 3D bounding box annotation3Dlayout : 3D room layout bounding box **************************************************************************************** Label: In SUNRGBDtoolbox/Metadata SUNRGBDMeta.mat: 2D,3D bounding box ground truth and image information for each frame. SUNRGBD2Dseg.mat: 2D segmetation ground truth. The index in "SUNRGBD2Dseg(imageId).seglabelall" mapping the name to "seglistall". The index in "SUNRGBD2Dseg(imageId).seglabel" are mapping the object name in "seg37list". **************************************************************************************** In SUNRGBDtoolbox/traintestsplit allsplit.mat: stores the training and testing split. **************************************************************************************** Code: SUNRGBDtoolbox/demo.m : Examples to load and visualize the data. SUNRGBDtoolbox/readframeSUNRGBD.m : Example code to read SUNRGBD annotation from ".json" file. Some of the code are modified base on RMRC 3D detection challenge. http://ttic.uchicago.edu/~rurtasun/rmrc/indoor.php ***************************************************************************************** Citation: Please cite our paper if you use this data: S. Song, S. Lichtenberg, and J. Xiao. SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2015) The dataset contains RGB-D images from NYU depth v2 [1], Berkeley B3DO [2], and SUN3D [3]. Besides this paper, you are required to also cite the following papers if you use this dataset: [1]N. Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. [2] A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell. A category-level 3-d object dataset: Putting the kinect to work. In ICCV Workshop on Consumer Depth Cameras for Computer Vision, 2011. [3] J. Xiao, A. Owens, and A. Torralba. SUN3D: A database of big spaces reconstructed using SfM and object labels. In ICCV, 2013