  We present a semi-supervised method for panoptic segmentation based on
ConsInstancy regularisation, a novel strategy for semi-supervised learning. It
leverages completely unlabelled data by enforcing consistency between predicted
instance representations and semantic segmentations during training in order to
improve the segmentation performance. To this end, we also propose new types of
instance representations that can be predicted by one simple forward path
through a fully convolutional network (FCN), delivering a convenient and
simple-to-train framework for panoptic segmentation. More specifically, we
propose the prediction of a three-dimensional instance orientation map as
intermediate representation and two complementary distance transform maps as
final representation, providing unique instance representations for a panoptic
segmentation. We test our method on two challenging data sets of both, hardened
and fresh concrete, the latter being proposed by the authors in this paper
demonstrating the effectiveness of our approach, outperforming the results
achieved by state-of-the-art methods for semi-supervised segmentation. In
particular, we are able to show that by leveraging completely unlabeled data in
our semi-supervised approach the achieved overall accuracy (OA) is increased by
up to 5% compared to an entirely supervised training using only labeled data.
Furthermore, we exceed the OA achieved by state-of-the-art semi-supervised
methods by up to 1.5%.
