  While a great variety of 3D cameras have been introduced in recent years,
most publicly available datasets for object recognition and pose estimation
focus on one single camera. In this work, we present a dataset of 32 scenes
that have been captured by 7 different 3D cameras, totaling 49,294 frames. This
allows evaluating the sensitivity of pose estimation algorithms to the
specifics of the used camera and the development of more robust algorithms that
are more independent of the camera model. Vice versa, our dataset enables
researchers to perform a quantitative comparison of the data from several
different cameras and depth sensing technologies and evaluate their algorithms
before selecting a camera for their specific task. The scenes in our dataset
contain 20 different objects from the common benchmark YCB object and model set
[1], [2]. We provide full ground truth 6DoF poses for each object, per-pixel
segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion
of each object. We have also performed an initial evaluation of the cameras
using our dataset on a state-of-the-art object recognition and pose estimation
system [3].
