Abstract | ||
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Spectral Monte-Carlo methods are currently the most powerful techniques for simulating light transport with wavelength-dependent phenomena (e.g., dispersion, colored particle scattering, or diffraction gratings). Compared to trichromatic rendering, sampling the spectral domain requires significantly more samples for noise-free images. Inspired by gradient-domain rendering, which estimates image gradients, we propose spectral gradient sampling to estimate the gradients of the spectral distribution inside a pixel. These gradients can be sampled with a significantly lower variance by carefully correlating the path samples of a pixel in the spectral domain, and we introduce a mapping function that shifts paths with wavelength-dependent interactions. We compute the result of each pixel by integrating the estimated gradients over the spectral domain using a one-dimensional screened Poisson reconstruction. Our method improves convergence and reduces chromatic noise from spectral sampling, as demonstrated by our implementation within a conventional path tracer. |
Year | DOI | Venue |
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2018 | 10.1111/cgf.13474 | COMPUTER GRAPHICS FORUM |
Field | DocType | Volume |
Computer vision,Computer science,Path tracing,Sampling (statistics),Artificial intelligence | Journal | 37.0 |
Issue | ISSN | Citations |
4.0 | 0167-7055 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Victor Petitjean | 1 | 0 | 0.68 |
Pablo Bauszat | 2 | 77 | 8.25 |
Elmar Eisemann | 3 | 35 | 6.55 |