Perception-based Optimization of Wavelength Sampling Distributions for Spectral Rendering
C. Dobos (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Elmar Eisemann – Mentor (TU Delft - Computer Graphics and Visualisation)
C.J. Peters – Mentor (TU Delft - Computer Graphics and Visualisation)
Michael Weinmann – Mentor (TU Delft - Computer Graphics and Visualisation)
G. Smaragdakis – Graduation committee member (TU Delft - Cyber Security)
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Abstract
Spectral Monte Carlo rendering stands as the gold standard for accurately simulating complex optical phenomena, such as fluorescence and chromatic dispersion, but typically exhibits slow convergence behavior due to challenges in sampling the wavelength domain. Convergence is even slower for scenes with complex spectral distributions, and hence a robust method for sampling the wavelength domain is crucial for better performance. To address this challenge, we propose a preprocessing step consisting of optimizing a set of distributions specifically for wavelength sampling, and we investigate whether this approach can achieve a lower perceptual error than traditional sampling techniques. Our evaluation indicates that our method can significantly reduce perceptual error in single-emitter scenes featuring a complex spectral power distribution (SPD), when compared to uniform sampling, without incurring any additional overhead during rendering.