  We present a dataset of 998 3D models of everyday tabletop objects along with
their 847,000 real world RGB and depth images. Accurate annotations of camera
poses and object poses for each image are performed in a semi-automated fashion
to facilitate the use of the dataset for myriad 3D applications like shape
reconstruction, object pose estimation, shape retrieval etc. We primarily focus
on learned multi-view 3D reconstruction due to the lack of appropriate real
world benchmark for the task and demonstrate that our dataset can fill that
gap. The entire annotated dataset along with the source code for the annotation
tools and evaluation baselines is available at
http://www.ocrtoc.org/3d-reconstruction.html.
