IEICE TRANSACTIONS on Information
Phase-Based Window Matching with Geometric Correction for Multi-View Stereo
Summary :
Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.10 pp.1818-1828
- Publication Date
- 2015/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7409
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Shuji SAKAI
Tohoku University
Koichi ITO
Tohoku University
Takafumi AOKI
Tohoku University
Takafumi WATANABE
Toppan Printing Co., Ltd.
Hiroki UNTEN
Toppan Printing Co., Ltd.
Keyword
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Shuji SAKAI, Koichi ITO, Takafumi AOKI, Takafumi WATANABE, Hiroki UNTEN, "Phase-Based Window Matching with Geometric Correction for Multi-View Stereo" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 10, pp. 1818-1828, October 2015, doi: 10.1587/transinf.2014EDP7409.
Abstract: Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7409/_p
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@ARTICLE{e98-d_10_1818,
author={Shuji SAKAI, Koichi ITO, Takafumi AOKI, Takafumi WATANABE, Hiroki UNTEN, },
journal={IEICE TRANSACTIONS on Information},
title={Phase-Based Window Matching with Geometric Correction for Multi-View Stereo},
year={2015},
volume={E98-D},
number={10},
pages={1818-1828},
abstract={Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.},
keywords={},
doi={10.1587/transinf.2014EDP7409},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Phase-Based Window Matching with Geometric Correction for Multi-View Stereo
T2 - IEICE TRANSACTIONS on Information
SP - 1818
EP - 1828
AU - Shuji SAKAI
AU - Koichi ITO
AU - Takafumi AOKI
AU - Takafumi WATANABE
AU - Hiroki UNTEN
PY - 2015
DO - 10.1587/transinf.2014EDP7409
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2015
AB - Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.
ER -
English
