  Since the resurgence of CNNs the robotic vision community has developed a
range of algorithms that perform classification, semantic segmentation and
structure prediction (depths, normals, surface curvature) using neural
networks. While some of these models achieve state-of-the art results and super
human level performance, deploying these models in a time critical robotic
environment remains an ongoing challenge. Real-time frameworks are of paramount
importance to build a robotic society where humans and robots integrate
seamlessly. To this end, we present a novel real-time structure prediction
framework that predicts depth at 30fps on an NVIDIA-TX2. At the time of
writing, this is the first piece of work to showcase such a capability on a
mobile platform. We also demonstrate with extensive experiments that neural
networks with very large model capacities can be leveraged in order to train
accurate condensed model architectures in a "from teacher to student" style
knowledge transfer.
