  In this paper we propose USegScene, a framework for semantically guided
unsupervised learning of depth, optical flow and ego-motion estimation for
stereo camera images using convolutional neural networks. Our framework
leverages semantic information for improved regularization of depth and optical
flow maps, multimodal fusion and occlusion filling considering dynamic rigid
object motions as independent SE(3) transformations. Furthermore, complementary
to pure photo-metric matching, we propose matching of semantic features,
pixel-wise classes and object instance borders between the consecutive images.
In contrast to previous methods, we propose a network architecture that jointly
predicts all outputs using shared encoders and allows passing information
across the task-domains, e.g., the prediction of optical flow can benefit from
the prediction of the depth. Furthermore, we explicitly learn the depth and
optical flow occlusion maps inside the network, which are leveraged in order to
improve the predictions in therespective regions. We present results on the
popular KITTI dataset and show that our approach outperforms other methods by a
large margin.
