  Depth estimation from single monocular images is a key component of scene
understanding and has benefited largely from deep convolutional neural networks
(CNN) recently. In this article, we take advantage of the recent deep residual
networks and propose a simple yet effective approach to this problem. We
formulate depth estimation as a pixel-wise classification task. Specifically,
we first discretize the continuous depth values into multiple bins and label
the bins according to their depth range. Then we train fully convolutional deep
residual networks to predict the depth label of each pixel. Performing discrete
depth label classification instead of continuous depth value regression allows
us to predict a confidence in the form of probability distribution. We further
apply fully-connected conditional random fields (CRF) as a post processing step
to enforce local smoothness interactions, which improves the results. We
evaluate our approach on both indoor and outdoor datasets and achieve
state-of-the-art performance.
