Deep Image-Based Adaptive BRDF Measure

Wen Cao

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Paper

Deep Image-Based Adaptive BRDF Measure

Topics: Lighting and Appearance; Modeling and Algorithms; Rendering Algorithms; Surface Modeling

In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 , HUCAPP and IVAPP: , 292-299, 2025 , Porto, Portugal

Author: Wen Cao

Affiliation: Media and Information Technology, Department of Science and Technology, Linköping University, SE-601 74 Norrköping, Sweden

Keyword(s): BRDF Measure, Adaptive, Deep Learning.

Abstract: Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in realistic image rendering. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel iterative method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. The method is a two-step approach, where the first step takes an image of the physical material as input and uses a lightweight neural network to estimate the parameters of an analytic BRDF model. The second step adaptive sample the measurements using the estimated BRDF model and an image loss to maximize the BRDF representation accuracy. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representation.

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Paper citation in several formats:

Cao, W. (2025). Deep Image-Based Adaptive BRDF Measure. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 292-299. DOI: 10.5220/0013201000003912

@conference{grapp25,
author={Wen Cao},
title={Deep Image-Based Adaptive BRDF Measure},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP},
year={2025},
pages={292-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013201000003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP
TI - Deep Image-Based Adaptive BRDF Measure
SN - 978-989-758-728-3
IS - 2184-4321
AU - Cao, W.
PY - 2025
SP - 292
EP - 299
DO - 10.5220/0013201000003912
PB - SciTePress