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This paper addresses the problem of estimating the depth map of a scene givena single RGB image. We propose a fully convolutional architecture, encompassingresidual learning, to model the ambiguous mapping between monocular images anddepth maps. In order to improve the output resolution, we present a novel wayto efficiently learn feature map up-sampling within the network. Foroptimization, we introduce the reverse Huber loss that is particularly suitedfor the task at hand and driven by the value distributions commonly present indepth maps. Our model is composed of a single architecture that is trainedend-to-end and does not rely on post-processing techniques, such as CRFs orother additional refinement steps. As a result, it runs in real-time on imagesor videos. In the evaluation, we show that the proposed model contains fewerparameters and requires fewer training data than the current state of the art,while outperforming all approaches on depth estimation. Code and models arepublicly available.

Deeper Depth Prediction with Fully Convolutional Residual Networks