Silencing the noise on
            <i>Elysium</i>

Goddard, L.C.

doi:10.1145/2614106.2614116

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…For example, Chaos Group's VRay renderer, the Corona renderer, and Pixar's RenderMan now ship with integrated denoisers. Moreover, many production houses are developing their own internal solutions [Goddard 2014] or using third-party tools (e.g., the Altus denoiser).…”

Section: Introductionmentioning

confidence: 99%

Kernel-predicting convolutional networks for denoising Monte Carlo renderings

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Kernel-predicting convolutional networks for denoising Monte Carlo renderings

et al. 2017

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“…A key advantage of image‐based methods is that their computational complexity scales with the number of pixels and not with the scene and lighting complexity. Consequently, such methods have become a vital part of production rendering pipelines [God14].…”

Section: Introductionmentioning

confidence: 99%

Path‐space Motion Estimation and Decomposition for Robust Animation Filtering

Zimmer

Rousselle

Jakob

et al. 2015

Computer Graphics Forum

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Renderings of animation sequences with physics‐based Monte Carlo light transport simulations are exceedingly costly to generate frame‐by‐frame, yet much of this computation is highly redundant due to the strong coherence in space, time and among samples. A promising approach pursued in prior work entails subsampling the sequence in space, time, and number of samples, followed by image‐based spatio‐temporal upsampling and denoising. These methods can provide significant performance gains, though major issues remain: firstly, in a multiple scattering simulation, the final pixel color is the composite of many different light transport phenomena, and this conflicting information causes artifacts in image‐based methods. Secondly, motion vectors are needed to establish correspondence between the pixels in different frames, but it is unclear how to obtain them for most kinds of light paths (e.g. an object seen through a curved glass panel). To reduce these ambiguities, we propose a general decomposition framework, where the final pixel color is separated into components corresponding to disjoint subsets of the space of light paths. Each component is accompanied by motion vectors and other auxiliary features such as reflectance and surface normals. The motion vectors of specular paths are computed using a temporal extension of manifold exploration and the remaining components use a specialized variant of optical flow. Our experiments show that this decomposition leads to significant improvements in three image‐based applications: denoising, spatial upsampling, and temporal interpolation.

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“…For example, Anderson and Meyer from Pixar proposed a statistical technique based on PCA to denoise animation sequences [MA06]. Goddard, from Image Engine, recently presented the denoising technique they developed for the production of Elysium [God14]. WDAS also used denoising in the production of Big Hero 6, which proved to be crucial for reaching production deadlines, and Pixar is currently investigating various denoising alternatives for its own productions.…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Zwicker

Jarosz

Lehtinen

et al. 2015

Computer Graphics Forum

116

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Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between "a priori" methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and "a posteriori" methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

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Silencing the noise on Elysium

Cited by 7 publications

References 3 publications

Kernel-predicting convolutional networks for denoising Monte Carlo renderings

Kernel-predicting convolutional networks for denoising Monte Carlo renderings

Path‐space Motion Estimation and Decomposition for Robust Animation Filtering

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

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