Abstract
We present a novel method that predicts a confidence to improve the accuracy of an estimated depth map in stereo matching. In contrast to existing learning based approaches relying on hand-crafted confidence features, we cast this problem into a convolutional neural network, learned using both a matching cost volume and its associated disparity map. As the size of the matching cost volume varies depending on a search range of stereo image pairs, we propose to use a top-K matching probability volume layer so that an input size for convolutional layers remains unchanged. Experimental results demonstrate that the proposed method outperforms the state-of-the-art confidence estimation approaches on various benchmarks.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 992-996 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509021758 |
| DOIs | |
| Publication status | Published - 2018 Feb 20 |
| Event | 24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 2017 Sep 17 → 2017 Sep 20 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| Volume | 2017-September |
| ISSN (Print) | 1522-4880 |
Other
| Other | 24th IEEE International Conference on Image Processing, ICIP 2017 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 17/9/17 → 17/9/20 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
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Deep stereo confidence prediction for depth estimation. / Kim, Sunok; Min, Dongbo; Ham, Bumsub; Kim, Seungryong; Sohn, Kwanghoon.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2018. p. 992-996 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep stereo confidence prediction for depth estimation
AU - Kim, Sunok
AU - Min, Dongbo
AU - Ham, Bumsub
AU - Kim, Seungryong
AU - Sohn, Kwanghoon
PY - 2018/2/20
Y1 - 2018/2/20
N2 - We present a novel method that predicts a confidence to improve the accuracy of an estimated depth map in stereo matching. In contrast to existing learning based approaches relying on hand-crafted confidence features, we cast this problem into a convolutional neural network, learned using both a matching cost volume and its associated disparity map. As the size of the matching cost volume varies depending on a search range of stereo image pairs, we propose to use a top-K matching probability volume layer so that an input size for convolutional layers remains unchanged. Experimental results demonstrate that the proposed method outperforms the state-of-the-art confidence estimation approaches on various benchmarks.
AB - We present a novel method that predicts a confidence to improve the accuracy of an estimated depth map in stereo matching. In contrast to existing learning based approaches relying on hand-crafted confidence features, we cast this problem into a convolutional neural network, learned using both a matching cost volume and its associated disparity map. As the size of the matching cost volume varies depending on a search range of stereo image pairs, we propose to use a top-K matching probability volume layer so that an input size for convolutional layers remains unchanged. Experimental results demonstrate that the proposed method outperforms the state-of-the-art confidence estimation approaches on various benchmarks.
UR - http://www.scopus.com/inward/record.url?scp=85045311133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045311133&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8296430
DO - 10.1109/ICIP.2017.8296430
M3 - Conference contribution
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 992
EP - 996
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
ER -