Conference paper
Authors list: Guarnera, Dar'ya; Guarnera, Giuseppe Claudio; Toscani, Matteo; Glencross, Mashhuda; Li, Baihua; Hardeberg, Jon Yngve; Gegenfurtner, Karl R.
Appeared in: ACM SIGGRAPH 2018 Talks
Publication year: 2018
eISBN: 978-1-4503-5820-0
DOI Link: https://doi.org/10.1145/3214745.3214807
Conference: SIGGRAPH '18: Special Interest Group on Computer Graphics and Interactive Techniques Conference
The need to manually match the appearance of a material in two or more different rendering tools is common in digital 3D product design, due to the wide range of tools and material models commonly used, and a lack of standards to exchange materials data. Since the effect of BRDF parameters on rendered images is non-uniform, visually matching to a reference is time consuming and error-prone. We present an automatic BRDF remapping technique to match the appearance of a source material model to a target, providing a mapping between their parameter spaces. Our framework, based on Genetic Algorithm optimization and an image space similarity metric, provides a faithful mapping among analytical BRDFs, even when the BRDF models are deeply different. Objective and perceptual evaluations confirm the efficacy of the framework.
Abstract:
Citation Styles
Harvard Citation style: Guarnera, D., Guarnera, G., Toscani, M., Glencross, M., Li, B., Hardeberg, J., et al. (2018) Perceptually validated analytical BRDFs parameters remapping, in ACM SIGGRAPH 2018 Talks. New York, NY: ACM. Article 17. https://doi.org/10.1145/3214745.3214807
APA Citation style: Guarnera, D., Guarnera, G., Toscani, M., Glencross, M., Li, B., Hardeberg, J., & Gegenfurtner, K. (2018). Perceptually validated analytical BRDFs parameters remapping. In ACM SIGGRAPH 2018 Talks. ACM. https://doi.org/10.1145/3214745.3214807
