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Optimal Activation Function for Anisotropic BRDF Modeling
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SYSNO ASEP 0569632 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Optimal Activation Function for Anisotropic BRDF Modeling Author(s) Mikeš, Stanislav (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP. - Lisbon : SciTePress, 2023 / Sousa A. Augusto ; Bashford-Rogers Thomas ; Bouatouch Kadi - ISSN 2184-4321 - ISBN 978-989-758-634-7 Pages s. 162-169 Number of pages 8 s. Publication form Print - P Action International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - GRAPP 2023 /18./ Event date 19.02.2023 - 21.02.2023 VEvent location Lisbon Country PT - Portugal Event type WRD Language eng - English Country PT - Portugal Keywords Anisotropic BRDF models ; neural network ; activation function ; BTF Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 DOI 10.5220/0011616200003417 Annotation We present simple and fast neural anisotropic Bidirectional Reflectance Distribution Function (NN-BRDF) efficient models, capable of accurately estimating unmeasured combinations of illumination and viewing angles from sparse Bidirectional Texture Function (BTF) measurement of neighboring points in the illumination/viewing hemisphere. Our models are optimized for the best-performing activation function from nineteen widely used nonlinear functions and can be directly used in rendering. We demonstrate that the activation function significantly influences the modeling precision. The models enable us to reach significant time and cost-saving in not trivial and costly BTF measurements while maintaining acceptably low modeling error. The presented models learn well, even from only three percent of the original BTF measurements, and we can prove this by precise evaluation of the modeling error, which is smaller than the errors of alternative analytical BRDF models. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2024
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