Learning descriptor, confidence, and depth estimation in multi-view stereo

Sungil Choi, Seungryong Kim, Kihong Park, Kwanghoon Sohn

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

1 Citation (Scopus)

Abstract

Depth estimation from multi-view stereo images is one of the most fundamental and essential tasks in understanding a scene imaginary. In this paper, we propose a machine learning technique based on deep convolutional neural networks (CNNs) for multi-view stereo matching. The proposed method measures the matching cost to extract depth values between two-view stereo images among multi-view stereo images using a deep architecture. Moreover, we present the confidence estimation network for incorporating the cost volumes along the depth hypothesis in multiview stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages389-395
Number of pages7
ISBN (Electronic)9781538661000
DOIs
Publication statusPublished - 2018 Dec 13
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: 2018 Jun 182018 Jun 22

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2018-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
CountryUnited States
CitySalt Lake City
Period18/6/1818/6/22

Fingerprint

Learning systems
Costs
Neural networks
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Choi, S., Kim, S., Park, K., & Sohn, K. (2018). Learning descriptor, confidence, and depth estimation in multi-view stereo. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (pp. 389-395). [8575527] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2018-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00065
Choi, Sungil ; Kim, Seungryong ; Park, Kihong ; Sohn, Kwanghoon. / Learning descriptor, confidence, and depth estimation in multi-view stereo. Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society, 2018. pp. 389-395 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).
@inproceedings{0867ff0c1ff643e2a6bef82e3c710915,
title = "Learning descriptor, confidence, and depth estimation in multi-view stereo",
abstract = "Depth estimation from multi-view stereo images is one of the most fundamental and essential tasks in understanding a scene imaginary. In this paper, we propose a machine learning technique based on deep convolutional neural networks (CNNs) for multi-view stereo matching. The proposed method measures the matching cost to extract depth values between two-view stereo images among multi-view stereo images using a deep architecture. Moreover, we present the confidence estimation network for incorporating the cost volumes along the depth hypothesis in multiview stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.",
author = "Sungil Choi and Seungryong Kim and Kihong Park and Kwanghoon Sohn",
year = "2018",
month = "12",
day = "13",
doi = "10.1109/CVPRW.2018.00065",
language = "English",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "389--395",
booktitle = "Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018",
address = "United States",

}

Choi, S, Kim, S, Park, K & Sohn, K 2018, Learning descriptor, confidence, and depth estimation in multi-view stereo. in Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018., 8575527, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-June, IEEE Computer Society, pp. 389-395, 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018, Salt Lake City, United States, 18/6/18. https://doi.org/10.1109/CVPRW.2018.00065

Learning descriptor, confidence, and depth estimation in multi-view stereo. / Choi, Sungil; Kim, Seungryong; Park, Kihong; Sohn, Kwanghoon.

Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society, 2018. p. 389-395 8575527 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2018-June).

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

TY - GEN

T1 - Learning descriptor, confidence, and depth estimation in multi-view stereo

AU - Choi, Sungil

AU - Kim, Seungryong

AU - Park, Kihong

AU - Sohn, Kwanghoon

PY - 2018/12/13

Y1 - 2018/12/13

N2 - Depth estimation from multi-view stereo images is one of the most fundamental and essential tasks in understanding a scene imaginary. In this paper, we propose a machine learning technique based on deep convolutional neural networks (CNNs) for multi-view stereo matching. The proposed method measures the matching cost to extract depth values between two-view stereo images among multi-view stereo images using a deep architecture. Moreover, we present the confidence estimation network for incorporating the cost volumes along the depth hypothesis in multiview stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.

AB - Depth estimation from multi-view stereo images is one of the most fundamental and essential tasks in understanding a scene imaginary. In this paper, we propose a machine learning technique based on deep convolutional neural networks (CNNs) for multi-view stereo matching. The proposed method measures the matching cost to extract depth values between two-view stereo images among multi-view stereo images using a deep architecture. Moreover, we present the confidence estimation network for incorporating the cost volumes along the depth hypothesis in multiview stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.

UR - http://www.scopus.com/inward/record.url?scp=85060856639&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060856639&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2018.00065

DO - 10.1109/CVPRW.2018.00065

M3 - Conference contribution

AN - SCOPUS:85060856639

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

SP - 389

EP - 395

BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

PB - IEEE Computer Society

ER -

Choi S, Kim S, Park K, Sohn K. Learning descriptor, confidence, and depth estimation in multi-view stereo. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society. 2018. p. 389-395. 8575527. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2018.00065