Data-driven single image depth estimation using weighted median statistics

Youngjung Kim, Sunghwan Choi, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 2014 Jan 28

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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