Computer Graphics Forum

Spectral Gradient Sampling for Path Tracing

Victor Petitjean, Pablo Bauszat, and Elmar Eisemann

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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.

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@article{bib:petitjean:2018,
    author       = { Petitjean, Victor and Bauszat, Pablo and Eisemann, Elmar },    
    title        = { Spectral Gradient Sampling for Path Tracing },
    journal      = { Computer Graphics Forum },
    volume       = { 37 },
    year         = { 2018 },
    pages        = { 45--53 },
    doi          = { 10.1111/cgf.13474 },
    dblp         = { journals/cgf/PetitjeanBE18 },
    url          = { https://publications.graphics.tudelft.nl/papers/207 },
}