SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite

Shuran Song, Samuel P. Lichtenberg, Jianxiong Xiao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 567-576

Abstract


Although RGB-D sensors have enabled major breakthroughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce 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 contains 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.

Related Material


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[bibtex]
@InProceedings{Song_2015_CVPR,
author = {Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},
title = {SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}