Reducing Photon Mapping Bandwidth by Query Reordering
Venue
IEEE Transactions on Visualization and Computer Graphics, vol. 14 (2008)
Publication Year
2008
Authors
Joshua Steinhurst, Greg Coombe, Anselmo Lastra
BibTeX
Abstract
Photon mapping places an enormous burden on the memory hierarchy. Rendering a
512×512 image of a simple scene can require more than 196GB of raw bandwidth to the
photon map data structure. This bandwidth is a major obstacle to real time photon
mapping. This paper investigates two approaches for reducing the required
bandwidth: 1) reordering the kNN searches; and 2) cache conscious data structures.
Using a Hilbert curve reordering, we demonstrate an experimental lower bound of
15MB of bandwidth for the same scene. Unfortunately, this improvement of four
orders of magnitude requires a prohibitive amount of intermediate storage. We
introduce two novel cost-effective algorithms that reduce the bandwidth by one
order of magnitude. Scenes of different complexities are shown to exhibit similar
reductions in bandwidth. We explain why the choice of data structure does not
achieve similar reductions. We also examine the interaction of query reordering
with two photon map acceleration techniques, importance sampling and the irradiance
cache. Query reordering exploits the additional coherence that arises from the use
of importance sampling in scenes with glossy surfaces. Irradiance caching also
benefits from query reordering, even when complex surface geometry reduces the
effectiveness of the irradiance cache.
