  Learning depth from a single image, as an important issue in scene
understanding, has attracted a lot of attention in the past decade. The
accuracy of the depth estimation has been improved from conditional Markov
random fields, non-parametric methods, to deep convolutional neural networks
most recently. However, there exist inherent ambiguities in recovering 3D from
a single 2D image. In this paper, we first prove the ambiguity between the
focal length and monocular depth learning, and verify the result using
experiments, showing that the focal length has a great influence on accurate
depth recovery. In order to learn monocular depth by embedding the focal
length, we propose a method to generate synthetic varying-focal-length dataset
from fixed-focal-length datasets, and a simple and effective method is
implemented to fill the holes in the newly generated images. For the sake of
accurate depth recovery, we propose a novel deep neural network to infer depth
through effectively fusing the middle-level information on the
fixed-focal-length dataset, which outperforms the state-of-the-art methods
built on pre-trained VGG. Furthermore, the newly generated varying-focal-length
dataset is taken as input to the proposed network in both learning and
inference phases. Extensive experiments on the fixed- and varying-focal-length
datasets demonstrate that the learned monocular depth with embedded focal
length is significantly improved compared to that without embedding the focal
length information.
