We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping.
We welcome Daniel Archambault to Swansea. Daniel has joined us as a permanent lecturer in the area of Visualization.
Welcome to Jingjing Deng who starts working here as a NISCHR BRU RA with Dr. Xianghua Xie on Interactive Medical Image Segmentation. Jingjing is pursuing a PhD degree at the same time.