Abstract
We describe a modified progressive photon mapping (PPM) method applied with sample elimination, referred to as progressive photon elimination, to pursue accurate results and accelerated iterations. Meanwhile, an elimination status tree is proposed for progressive photon elimination, which retains the information used in the elimination and can be updated along with the multi-pass process to solve the challenge of the accuracy of local parameters. By using the status tree, our method can obtain a uniform photon distribution at each iteration by eliminating a certain number of photons. The tree is also improved to adapt to the complicated photon distributions. This strategy also facilitates the parallel elimination with a priori elimination ratio in the status tree, making the algorithm further accelerated. In parallel block processing, the equilibrium and edge problems are resolved. The experimental results show that our method requires about half of the number of iterations to achieve the same visual effect compared with progressive photon mapping.
Highlights
Global illumination is essential for realistic image rendering
This strategy is appropriate for almost all of the improved progressive photon mapping (PPM) methods, including Stochastic PPM [4], because it is an additional step between photon tracing and radiance estimation
The rendering results show that our PPE yields better visual effects with fewer errors from the reference image compared with the PPM with the same number of iterations
Summary
Global illumination is essential for realistic image rendering. Photon mapping(PM) [2] is one of the most widely used algorithms, because it is capable of computing a full global illumination effects such as shadows, caustics, and indirect illumination. Photon relaxation as a pre-pass between photon tracing and illumination reconstruction removes noise directly from the distribution, and this kind of method is useful rendering caustics. We design a more suitable elimination method for PPM, and we further create an elimination status tree to provide progressive results. This strategy is appropriate for almost all of the improved PPM methods, including Stochastic PPM [4], because it is an additional step between photon tracing and radiance estimation. By using this strategy, PPM methods acquire a great speedup for the accurate global illumination
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