Technical Papers
Global Illumination
Wednesday, 24 July 9:00 AM - 10:30 AM
Session Chair: Holly Rushmeier, Yale University
Wednesday, 24 July 9:00 AM - 10:30 AM
Session Chair: Holly Rushmeier, Yale University
A new photon-tracing method that takes the visibility of each photon path into account using an adaptive Markov chain Monte Carlo method.
Toshiya Hachisuka
Aarhus Universitet
Henrik Wann Jensen
University of California, San Diego
A locally adaptive data‐driven error-minimization technique that optimally balances noise and bias in rendered images. The method derives asymptotic rates of convergence for progressive photon mapping.
Anton Kaplanyan
Karlsruher Institut für Technologie
Carsten Dachsbacher
Karlsruher Institut für Technologie
This novel Metropolis rendering algorithm first computes image gradients along with a low-fidelity approximation of the image, and then reconstructs the final image by solving a Poisson equation. As an extension of path-space Metropolis light transport, the algorithm is well suited for difficult transport scenarios.
Jaakko Lehtinen
NVIDIA Research
Tero Karras
NVIDIA Research
Samuli Laine
NVIDIA Research
Miika Aittala
Aalto University, NVIDIA Research
Frédo Durand
Massachusetts Institute of Technology
Timo Aila
NVIDIA Research
This new algorithm for interactive rendering of physically based global illumination, based on a novel frequency analysis of indirect lighting, combines adaptive sampling by Monte Carlo path tracing with real-time reconstruction of the resulting noisy images. Analysis assumes diffuse indirect lighting, with general receiver BRDF.
Soham Uday Mehta
University of California, Berkeley
Brandon Wang
University of California, Berkeley
Ravi Ramamoorthi
University of California, Berkeley
Frédo Durand
Massachusetts Institute of Technology