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Multi-scale deep CNNs have been used successfully for problems mapping eachpixel to a label, such as depth estimation and semantic segmentation. It hasalso been shown that such architectures are reusable and can be used formultiple tasks. These networks are typically trained independently for eachtask by varying the output layer(s) and training objective. In this work wepresent a new model for simultaneous depth estimation and semantic segmentationfrom a single RGB image. Our approach demonstrates the feasibility of trainingparts of the model for each task and then fine tuning the full, combined modelon both tasks simultaneously using a single loss function. Furthermore wecouple the deep CNN with fully connected CRF, which captures the contextualrelationships and interactions between the semantic and depth cues improvingthe accuracy of the final results. The proposed model is trained and evaluatedon NYUDepth V2 dataset outperforming the state of the art methods on semanticsegmentation and achieving comparable results on the task of depth estimation.

Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks