Spotted: Gradient-domain metropolis light transport

Sampling gradients ( change in color over space ) rather than colors. Some of our prior work involved gradient sampling. 


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Gradient-domain metropolis light transport

Jaakko Lehtinen, Tero Karras, Samuli Laine, Miika Aittala, Frédo Durand, Timo Aila

We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling.