Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we haven not achieved a similar performance jump for high-level scene understanding. Perhaps one of the main reasons for this is the lack of a benchmark of reasonable size with 3D annotations for training and 3D metrics for evaluation. In this paper, we present an RGB-D benchmark suite for the goal of advancing the state-of-the-art in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,000 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 58,657 3D bounding boxes with accurate object orientations, as well as a 3D room layout and category for scenes. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using direct and meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
 N. Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.
 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.
 J. Xiao, A. Owens, and A. Torralba. SUN3D: A database of big spaces reconstructed using SfM and object labels. In ICCV, 2013
This work is supported by gift funds from Intel Corporation. We thank Thomas Funkhouser, Jitendra Malik, Alexi A. Efros and Szymon Rusinkiewicz for valuable discussion. We also thank Linguang Zhang, Fisher Yu, Yinda Zhang, Luna Song, Pingmei Xu and Guoxuan Zhang for capturing and labeling.
 B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva Learning Deep Features for Scene Recognition using Places Database Advances in Neural Information Processing Systems 27 (NIPS2014)