Hierarchical Reconstruction of Quadtree-Based Approximations of Incident Radiance

Abstract

This thesis presents a method of hierarchically reconstructing quadtree-based approximations of incident radiance for path guiding. To that end, Gaussian denoising is applied by mapping quadtree data to matrices, performing regular matrix convolutions, and then mapping the data back to the quadtree. The reconstructed guiding distributions are shown to outperform previous work in most cases, especially when the number of path samples is limited. Additionally, using a simple target distribution in the beginning of the learning process before switching to the full version is shown to speed up the learning. To limit the overhead of the reconstruction procedure, an automatic workload budgeting algorithm is presented. While the improved quality of the guiding distributions seems promising, the implemented reconstruction algorithm imposes a large enough overhead to nearly cancel out the benefits.