2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Single-Image Depth Estimation
Based on Fourier Domain Analysis

Jae-Han Lee
Korea University

Minhyeok Heo
Korea University

Kyung-Rae Kim
Korea University

Chang-Su Kim
Korea University


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.


Jae-Han Lee, Minhyeok Heo, Kyung-Rae Kim, and Chang-Su Kim, "Single-Image Depth Estimation Based on Fourier Domain Analysis.


You may download the experimental results from here. Code of algorithm is available here.