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

Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach