We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error
loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in the presence of noise.