Non-Parametric Learning-based Depth Estimation from a Single Image in the Fourier Domain

Abstract

Plausible depth prediction from a single image is a challenging task in computer vision. This paper proposes a non-parametric learning-based single image depth prediction framework in the Fourier domain. Specifically, the candidate depth maps are retrieved from a large RGBD (RGB image+Depth) dataset and transferred to the input image based on the Coarse-to-fine PatchMatch (CPM) algorithm. Then, a Fourier transform-based depth fusion strategy is designed, in which the Discrete Fourier Transform (DFT) coefficients of the warped candidate depth maps are extracted and integrated via confidence analysis. Reliable depth map of the input image can be reconstructed based on the fused DFT vectors by using the inverse Fourier transform. Experimental results demonstrate that the proposed algorithm outperforms other depth estimation methods, and is very effective for inferring convincing depth maps.