SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
An improved illumination model for shaded display
Communications of the ACM
Realistic image synthesis using photon mapping
Realistic image synthesis using photon mapping
SaarCOR: a hardware architecture for ray tracing
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
"Kilauea": parallel global illumination renderer
Parallel Computing - Parallel graphics and visualisation
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Physically Based Rendering: From Theory to Implementation
Physically Based Rendering: From Theory to Implementation
RPU: a programmable ray processing unit for realtime ray tracing
ACM SIGGRAPH 2005 Papers
Distributed Interactive Ray Tracing of Dynamic Scenes
PVG '03 Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics
Ray tracing deformable scenes using dynamic bounding volume hierarchies
ACM Transactions on Graphics (TOG)
A memory-efficient scheme for fast spectral photon mapping
CGIM '07 Proceedings of the Ninth IASTED International Conference on Computer Graphics and Imaging
Realtime caustics using distributed photon mapping
EGSR'04 Proceedings of the Fifteenth Eurographics conference on Rendering Techniques
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Photon mapping attracts much attention as an excellent image generation technique that can simulate various lighting effects such as indirect illumination and caustics obtained only by the global illumination model. Although photon mapping can generate high-quality images, it requires more expensive calculations and a large memory capacity. In this paper, we present a new parallel photon mapping algorithm to solve the problems regarding the computing time and memory requirement. The proposed algorithm can effectively parallelize photon map construction and photon search by distributing partial photon maps among processing elements of a parallel computer. As a photon map is partitioned, only a part of the photon map is assigned to each processing element. Therefore, each processing element does not require a large memory space even if the entire photon map is quite huge. We implement the proposed algorithm using MPI and evaluate it through experiments on a parallel computer. The experimental results indicate that our algorithm can significantly reduce the rendering time of photon mapping as the number of processing elements increases, and can also save the memory space.