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.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3808-3812 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479957514 |
| DOIs | |
| Publication status | Published - 2014 Jan 28 |
Publication series
| Name | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
|---|
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
Cite this
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Data-driven single image depth estimation using weighted median statistics. / Kim, Youngjung; Choi, Sunghwan; Sohn, Kwanghoon.
2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3808-3812 7025773 (2014 IEEE International Conference on Image Processing, ICIP 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Data-driven single image depth estimation using weighted median statistics
AU - Kim, Youngjung
AU - Choi, Sunghwan
AU - Sohn, Kwanghoon
PY - 2014/1/28
Y1 - 2014/1/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84949927541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949927541&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2014.7025773
DO - 10.1109/ICIP.2014.7025773
M3 - Conference contribution
AN - SCOPUS:84949927541
T3 - 2014 IEEE International Conference on Image Processing, ICIP 2014
SP - 3808
EP - 3812
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -