Abstract

Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruc-tion, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main rea-sons is the lack of a large-scale benchmark with 3D anno-tations and 3D evaluation metrics. In this paper, we intro-duce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and con-tains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias. 1.