  Scene understanding is essential in determining how intelligent robotic
grasping and manipulation could get. It is a problem that can be approached
using different techniques: seen object segmentation, unseen object
segmentation, or 6D pose estimation. These techniques can even be extended to
multi-view. Most of the work on these problems depends on synthetic datasets
due to the lack of real datasets that are big enough for training and merely
use the available real datasets for evaluation.
  This encourages us to introduce a new dataset (called DoPose-6D). The dataset
contains annotations for 6D Pose estimation, object segmentation, and
multi-view annotations, which serve all the pre-mentioned techniques. The
dataset contains two types of scenes bin picking and tabletop, with the primary
motive for this dataset collection being bin picking.
  We illustrate the effect of this dataset in the context of unseen object
segmentation and provide some insights on mixing synthetic and real data for
the training. We train a Mask R-CNN model that is practical to be used in
industry and robotic grasping applications. Finally, we show how our dataset
boosted the performance of a Mask R-CNN model.
  Our DoPose-6D dataset, trained network models, pipeline code, and ROS driver
are available online.
