SUN RGB-D is a large-scale RGB-D (color + depth) indoor scene understanding benchmark introduced by Song, Lichtenberg, and Xiao. The dataset contains 10,335 real RGB-D images captured by four different sensors and densely annotated for a variety of scene-understanding tasks. Annotations include 146,617 2D polygons, tens of thousands of 3D bounding boxes (the paper reports 64,595 3D boxes), object orientations, 3D room layouts and scene categories. The dataset provides train/test splits (commonly reported as 5,285 train and 5,050 test images) and is used for tasks such as 2D/3D object detection, semantic/instance segmentation, scene classification and depth-related evaluations. Official dataset pages and the CVPR 2015 paper provide download links, annotation details and evaluation toolkits.
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