  Robust geometric and semantic scene understanding is ever more important in
many real-world applications such as autonomous driving and robotic navigation.
In this paper, we propose a multi-task learning-based approach capable of
jointly performing geometric and semantic scene understanding, namely depth
prediction (monocular depth estimation and depth completion) and semantic scene
segmentation. Within a single temporally constrained recurrent network, our
approach uniquely takes advantage of a complex series of skip connections,
adversarial training and the temporal constraint of sequential frame recurrence
to produce consistent depth and semantic class labels simultaneously. Extensive
experimental evaluation demonstrates the efficacy of our approach compared to
other contemporary state-of-the-art techniques.
