Data-driven single image depth estimation using weighted median statistics

Youngjung Kim, Sunghwan Choi, Kwanghoon Sohn

Abstract: In this paper, a data-driven approach is proposed for automatically estimating a plausible depth map from a single monocular image based on the weighted median statistics (WMS). Instead of using complicated parametric models for learning frameworks that are typically employed in existing methods, we cast the estimation as a simple yet effective statistical approach. It assigns perceptually proper depth values to an input image in accordance with a data-driven depth prior. Based on the assumption that similar scenes are likely to have similar depth structure, the depth prior is computed from the WMS of k-nearest neighbor 3D pairs in a large 3D image repository. We show that the WMS captures the underlying depth structure of the input image very well, even though the visual appearance of nearest neighbor images are not tightly aligned. The depth map is then inferred according to the depth prior by making use of the edge-aware image filtering technique, resulting in a discontinuity-preserving smooth depth map. Experimental results demonstrate that our method outperforms state-of-the-art methods in terms of both accuracy and efficiency.