  For most industrial bin picking solutions, the pose of a workpiece is
localized by matching a CAD model to point cloud obtained from 3D sensor.
Distinguishing flat workpieces from bottom of the bin in point cloud imposes
challenges in the localization of workpieces that lead to wrong or phantom
detections. In this paper, we propose a framework that solves this problem by
automatically segmenting workpiece regions from non-workpiece regions in a
point cloud data. It is done in real time by applying a fully convolutional
neural network trained on both simulated and real data. The real data has been
labelled by our novel technique which automatically generates ground truth
labels for real point clouds. Along with real time workpiece segmentation, our
framework also helps in improving the number of detected workpieces and
estimating the correct object poses. Moreover, it decreases the computation
time by approximately 1s due to a reduction of the search space for the object
pose estimation.
