Technical Papers

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Anisotropic Gaussian Mutations for Metropolis Light Transport through Hessian-Hamiltonian Dynamics

Wednesday, 04 November: 11:00 - 12:45

We present a novel Markov Chain Monte Carlo (MCMC) rendering algorithm that incorporates the second-order derivatives or Hessian information of the path throughput function, by using Hamiltonian dynamics. We show that the algorithm results in traditional Metropolis-Hastings sampling with anisotropic Gaussian mutations.

Tzu-Mao Li, Massachusetts Institute of Technology (MIT)
Jaakko Lehtinen, Aalto University, NVIDIA
Ravi Ramamoorthi, University of California, San Diego
Wenzel Jakob, ETH Zurich
Fredo Durand, Massachusetts Institute of Technology (MIT)

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