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Deployment of deep learning models in robotics as sensory informationextractors can be a daunting task to handle, even using generic GPU cards.Here, we address three of its most prominent hurdles, namely, i) the adaptationof a single model to perform multiple tasks at once (in this work, we considerdepth estimation and semantic segmentation crucial for acquiring geometric andsemantic understanding of the scene), while ii) doing it in real-time, and iii)using asymmetric datasets with uneven numbers of annotations per each modality.To overcome the first two issues, we adapt a recently proposed real-timesemantic segmentation network, making changes to further reduce the number offloating point operations. To approach the third issue, we embrace a simplesolution based on hard knowledge distillation under the assumption of havingaccess to a powerful `teacher' network. We showcase how our system can beeasily extended to handle more tasks, and more datasets, all at once,performing depth estimation and segmentation both indoors and outdoors with asingle model. Quantitatively, we achieve results equivalent to (or better than)current state-of-the-art approaches with one forward pass costing just 13ms and6.5 GFLOPs on 640x480 inputs. This efficiency allows us to directly incorporatethe raw predictions of our network into the SemanticFusion framework for dense3D semantic reconstruction of the scene.

Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations