Into the Blue: Better Caustics through Photon Relaxation

Abstract

The photon mapping method is one of the most popular algorithms employed in computer graphics today. However, obtaining good results is dependent on several variables including kernel shape and bandwidth, as well as the properties of the initial photon distribution. While the photon density estimation problem has been the target of extensive research, most algorithms focus on new methods of optimising the kernel to minimise noise and bias. In this paper we break from convention and propose a new approach that directly redistributes the underlying photons. We show that by relaxing the initial distribution into one with a blue noise spectral signature we can dramatically reduce background noise, particularly in areas of uniform illumination. In addition, we propose an efficient heuristic to detect and preserve features and discontinuities. We then go on to demonstrate how reconfiguration also permits the use of very low bandwidth kernels, greatly improving render times whilst reducing bias.