Deep stereo confidence prediction for depth estimation

Sunok Kim; Dongbo Min; Bumsub Ham; Seungryong Kim; Kwanghoon Sohn

doi:10.1109/ICIP.2017.8296430

Deep stereo confidence prediction for depth estimation

Sunok Kim, Dongbo Min, Bumsub Ham, Seungryong Kim, Kwanghoon Sohn

Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Scopus citations

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	https://doi.org/10.1109/ICIP.2017.8296430
State	Published - 2 Jul 2017
Event	24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 17 Sep 2017 → 20 Sep 2017

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2017-September
ISSN (Print)	1522-4880

Conference

Conference	24th IEEE International Conference on Image Processing, ICIP 2017
Country/Territory	China
City	Beijing
Period	17/09/17 → 20/09/17

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2016R1A2A2A05921659).

Publisher Copyright:
© 2017 IEEE.

Keywords

Confidence prediction
Convolutional neural networks
Depth refinement
Matching probability
Stereo matching

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10.1109/ICIP.2017.8296430

Cite this

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title = "Deep stereo confidence prediction for depth estimation",

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.",

keywords = "Confidence prediction, Convolutional neural networks, Depth refinement, Matching probability, Stereo matching",

author = "Sunok Kim and Dongbo Min and Bumsub Ham and Seungryong Kim and Kwanghoon Sohn",

note = "Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2016R1A2A2A05921659). Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",

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doi = "10.1109/ICIP.2017.8296430",

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Kim, S, Min, D, Ham, B, Kim, S & Sohn, K 2017, Deep stereo confidence prediction for depth estimation. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 992-996, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/09/17. https://doi.org/10.1109/ICIP.2017.8296430

Deep stereo confidence prediction for depth estimation. / Kim, Sunok; Min, Dongbo; Ham, Bumsub et al.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2017. p. 992-996 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kim, Sunok

AU - Min, Dongbo

AU - Ham, Bumsub

AU - Kim, Seungryong

AU - Sohn, Kwanghoon

N1 - Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2016R1A2A2A05921659). Publisher Copyright: © 2017 IEEE.

PY - 2017/7/2

Y1 - 2017/7/2

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.

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KW - Convolutional neural networks

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M3 - Conference contribution

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BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings

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Deep stereo confidence prediction for depth estimation

Abstract

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Keywords

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