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|Abstract:||Although RGB-D sensors have enabled major break-throughs 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.|
|Citation:||Song, Shuran, Samuel P. Lichtenberg, and Jianxiong Xiao. "SUN RGB-D: A RGB-D scene understanding benchmark suite." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015): pp. 567-576. doi:10.1109/CVPR.2015.7298655|
|Pages:||567 - 576|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
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