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Authors: Kebin Peng ; John Quarles and Kevin Desai

Affiliation: Department of Computer Science, The University of Texas at San Antonio, Texas, U.S.A.

Keyword(s): Bidirectional Reflectance Distribution Function, Irradiance, Radiometric Differences, Stereo Matching.

Abstract: Existing stereo matching methods assume that the corresponding pixels between left and right views have similar intensity. However, in real situations, image intensity tends to be dissimilar because of the radiometric differences obtained due to change in light reflected. In this paper, we propose a novel approach for removing these radiometric differences to perform stereo matching effectively. The approach estimates irradiance images based on the Bidirectional Reflectance Distribution Function (BRDF) which describes the ratio of radiance to irradiance for a given image. We demonstrate that to compute an irradiance image we only need to estimate the light source direction and the object’s roughness. We consider an approximation that the dot product of the unknown light direction parameters follows a Gaussian distribution and we use that to estimate the light source direction. The object’s roughness is estimated by calculating the pixel intensity variance using a local window strateg y. By applying the above steps independently on the original stereo images, we obtain the illumination invariant irradiance images that can be used as input to stereo matching methods. Experiments conducted on well-known stereo estimation datasets demonstrate that our proposed approach significantly reduces the error rate of stereo matching methods. (More)

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Paper citation in several formats:
Peng, K.; Quarles, J. and Desai, K. (2022). BRDF-based Irradiance Image Estimation to Remove Radiometric Differences for Stereo Matching. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 734-744. DOI: 10.5220/0010879800003124

@conference{visapp22,
author={Kebin Peng. and John Quarles. and Kevin Desai.},
title={BRDF-based Irradiance Image Estimation to Remove Radiometric Differences for Stereo Matching},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={734-744},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010879800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - BRDF-based Irradiance Image Estimation to Remove Radiometric Differences for Stereo Matching
SN - 978-989-758-555-5
IS - 2184-4321
AU - Peng, K.
AU - Quarles, J.
AU - Desai, K.
PY - 2022
SP - 734
EP - 744
DO - 10.5220/0010879800003124
PB - SciTePress

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