Abstract
We propose a deep learning algorithm for single-image
depth estimation based on the Fourier frequency domain
analysis. First, we develop a convolutional neural network
structure and propose a new loss function, called depthbalanced Euclidean loss, to train the network reliably for
a wide range of depths. Then, we generate multiple depth
map candidates by cropping input images with various
cropping ratios. In general, a cropped image with a small
ratio yields depth details more faithfully, while that with a
large ratio provides the overall depth distribution more reliably. To take advantage of these complementary properties, we combine the multiple candidates in the frequency
domain. Experimental results demonstrate that proposed
algorithm provides the state-of-art performance. Furthermore, through the frequency domain analysis, we validate
the efficacy of the proposed algorithm in most frequency
bands