On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches
MCML Authors
Florian Hofherr
Daniel Cremers
Prof. Dr.
Director
Abstract
Florian Hofherr
Daniel Cremers
Prof. Dr.
Director
Abstract
The bidirectional reflectance distribution function (BRDF) is an essential tool to capture the complex interaction of light and matter. Recently, several works have employed neural methods for BRDF modeling, following various strategies, ranging from utilizing existing parametric models to purely neural parametrizations. While all methods yield impressive results, a comprehensive comparison of the different approaches is missing in the literature. In this work, we present a thorough evaluation of several approaches, including results for qualitative and quantitative reconstruction quality and an analysis of reciprocity and energy conservation. Moreover, we propose two extensions that can be added to existing approaches: A novel additive combination strategy for neural BRDFs that split the reflectance into a diffuse and a specular part, and an input mapping that ensures reciprocity exactly by construction, while previous approaches only ensure it by soft constraints.
inproceedings HHC25
WACV 2025
IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson, AZ, USA, Feb 28-Mar 04, 2025. Oral Presentation.
Authors
F. Hofherr • B. Haefner • D. CremersLinks
DOIIn Collaboration
NVIDIA
Research Area
BibTeXKey: HHC25