Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection

Tomoki Ichikawa, Yoshiki Fukao, Shohei Nobuhara, Ko Nishino; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 16489-16497

Abstract


Computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, which turns out to be physically incompatible and limited in applicability. In this paper, we derive a novel analytical reflectance model, which we refer to as Fresnel Microfacet BRDF model, that is physically accurate and generalizes to various real-world surfaces. Our key idea is to model the Fresnel reflection and transmission of the surface microgeometry with a collection of oriented mirror facets, both for body and surface reflections. We carefully derive the Fresnel reflection and transmission for each microfacet as well as the light transport between them in the subsurface. This physically-grounded modeling also allows us to express the polarimetric behavior of reflected light in addition to its radiometric behavior. That is, FMBRDF unifies not only body and surface reflections but also light reflection in radiometry and polarization and represents them in a single model. Experimental results demonstrate its effectiveness in accuracy, expressive power, image-based estimation, and geometry recovery.

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[bibtex]
@InProceedings{Ichikawa_2023_CVPR, author = {Ichikawa, Tomoki and Fukao, Yoshiki and Nobuhara, Shohei and Nishino, Ko}, title = {Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {16489-16497} }