  In bin-picking scenarios, multiple instances of an object of interest are
stacked in a pile randomly, and hence, the instances are inherently subjected
to the challenges: severe occlusion, clutter, and similar-looking distractors.
Most existing methods are, however, for single isolated object instances, while
some recent methods tackle crowd scenarios as post-refinement which accounts
multiple object relations. In this paper, we address recovering 6D poses of
multiple instances in bin-picking scenarios in depth modality by multi-task
learning in deep neural networks. Our architecture jointly learns multiple
sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects;
and joint registration of multiple objects. For training data generation, depth
images of physically plausible object pose configurations are generated by a 3D
object model in a physics simulation, which yields diverse occlusion patterns
to learn. We adopt a state-of-the-art object detector, and 2D offsets are
further estimated via a network to refine misaligned 2D detections. The depth
and 3D pose estimator is designed to generate multiple hypotheses per
detection. This allows the joint registration network to learn occlusion
patterns and remove physically implausible pose hypotheses. We apply our
architecture on both synthetic (our own and Sileane dataset) and real (a public
Bin-Picking dataset) data, showing that it significantly outperforms
state-of-the-art methods by 15-31% in average precision.
