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Abstract:
Photon mapping is one of the most important algorithms for computing global
illumination. Especially for effi- ciently producing convincing caustics, there
are no real alternatives to photon mapping. On the other hand, photon mapping
is also quite costly: Each radiance lookup requires to find the k nearest
neighbors in a kd-tree, which can be more costly than shooting several rays.
Therefore, the nearest-neighbor queries often dominate the rendering time of a
photon map based renderer.
In this paper, we present a method that reorganizes i.e. unbalances the
kd-tree for storing the photons in a way that allows for finding the k-nearest
neighbors much more efficiently, thereby accelerating the radiance estimates by
a factor of 1.2 3.4. Most importantly, our method still finds exactly the same
k-nearest-neighbors as the original method, without introducing any
approximations or loss of accuracy. The impact of our method is demonstrated
with several practical examples.
