  In this paper, we introduce a new public dataset for 6D object pose
estimation and instance segmentation for industrial bin-picking. The dataset
comprises both synthetic and real-world scenes. For both, point clouds, depth
images, and annotations comprising the 6D pose (position and orientation), a
visibility score, and a segmentation mask for each object are provided. Along
with the raw data, a method for precisely annotating real-world scenes is
proposed. To the best of our knowledge, this is the first public dataset for 6D
object pose estimation and instance segmentation for bin-picking containing
sufficiently annotated data for learning-based approaches. Furthermore, it is
one of the largest public datasets for object pose estimation in general. The
dataset is publicly available at http://www.bin-picking.ai/en/dataset.html.
