An adaptive representation of spectral data for reflectance computations

Rougeron, Gilles; Péroche, Bernard

doi:10.1007/978-3-7091-6858-5_12

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…Other basis functions like e.g. the Fourier basis [Pee93] or Gaussian [Mey88] can be used, or a specialized basis out of the set of spectra of a given scene [DF03, BDM09,DMCP94,RP97] can be extracted. The Fourier basis can work well for arbitrary scenes if only smooth spectra occur.…”

Section: State Of the Art And Backgroundmentioning

confidence: 99%

Hero Wavelength Spectral Sampling

Wilkie

Nawaz²,

Droske³

et al. 2014

Computer Graphics Forum

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Figure 1: Wavelength-dependent subsurface scattering with single wavelength sampling (left) and our proposed method (right). The lower image halves are rendered with 4 paths per pixel, and the upper halves are rendered with 1024. For both sample counts, the hero wavelength images took only 3.5% longer than the single wavelength ones. AbstractWe present a spectral rendering technique that offers a compelling set of advantages over existing approaches.The key idea is to propagate energy along paths for a small, constant number of changing wavelengths. The first of these, the hero wavelength, is randomly sampled for each path, and all directional sampling is solely based on it. The additional wavelengths are placed at equal distances from the hero wavelength, so that all path wavelengths together always evenly cover the visible range. A related technique, spectral multiple importance sampling, was already introduced a few years ago. We propose a simplified and optimised version of this approach which is easier to implement, has good performance characteristics, and is actually more powerful than the original method. Our proposed method is also superior to techniques which use a static spectral representation, as it does not suffer from any inherent representation bias. We demonstrate the performance of our method in several application areas that are of critical importance for production work, such as fidelity of colour reproduction, sub-surface scattering, dispersion and volumetric effects. We also discuss how to couple our proposed approach with several technologies that are important in current production systems, such as photon maps, bidirectional path tracing, environment maps, and participating media.

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Section: State Of the Art And Backgroundmentioning

confidence: 99%

Hero Wavelength Spectral Sampling

Wilkie

Nawaz²,

Droske³

et al. 2014

Computer Graphics Forum

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“…The spectral radiance distribution I is often stored in discretized form using a set of uniformly distributed bins, each representing a range of wavelengths. More adaptive representations of the spectral distribution use sets of basis functions, e. g. Gaussian [Mey88], the Fourier‐basis [Pee93], or specialized basis functions [DMCP94, RP97, DF03, BDM09]. In the discrete case, there is a straightforward analogy between the classic image gradient and our proposed spectral gradient; the former is the finite difference between two pixels, while the latter is the difference between two bins.…”

Section: Related Workmentioning

confidence: 99%

Spectral Gradient Sampling for Path Tracing

Petitjean

Bauszat

Eisemann

2018

Computer Graphics Forum

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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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“…These researches have included linear modeling [Drew and Funt 1992;Finlayson et al 1994;Forsyth 1990;Horn 1984;Maloney 1986], the polynomial method [Geist et al 1996;Raso and Fournier 1991], a perceptually based approach . [Meyer 1988], an adaptive method [Deville et al 1994;Iehl and Péroche 2000;Rougeron and Peroche 1997], the composite method [Sun et al 1998;Sun et al 2000a], and a stratified wavelength clustering strategy [Evans and McCool 1999].…”

Section: Spectral Renderingmentioning

confidence: 99%

Rendering biological iridescences with RGB-based renderers

Sun

2006

ACM Trans. Graph.

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Brilliant iridescent colors occur on many biological objects. Current RGB-based graphics renderers are not sufficient to simulate such phenomena. This is because biological iridescences are caused by interference or diffraction, which requires wavelength information to describe. In this article, we propose an iridescent shading process that allows to render biological iridescences with RGB-based renderers. The key ideas are to construct spectra from colors and to use a wavelength-dependent model to describe iridescences. A novel model for iridescent illumination due to multilayer interference is developed based on analytic calculation and numerical simulation, and is simplified for practical rendering. The iridescent shading process is implemented using RenderMan embedded in Maya. Iridescent Morpho butterflies and ground beetles are rendered as examples to test the proposed techniques.

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An adaptive representation of spectral data for reflectance computations

Cited by 16 publications

References 3 publications

Hero Wavelength Spectral Sampling

Hero Wavelength Spectral Sampling

Spectral Gradient Sampling for Path Tracing

Rendering biological iridescences with RGB-based renderers

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