EG 2019 - STARs (CGF 38-2)

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632752

Browse

Browsing EG 2019 - STARs (CGF 38-2) by Subject "Computing methodologies"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Analysis of Sample Correlations for Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Singh, Gurprit; Ă–ztireli, Cengiz; Ahmed, Abdalla G. M.; Coeurjolly, David; Subr, Kartic; Deussen, Oliver; Ostromoukhov, Victor; Ramamoorthi, Ravi; Jarosz, Wojciech; Giachetti, Andrea and Rushmeyer, Holly
    Modern physically based rendering techniques critically depend on approximating integrals of high dimensional functions representing radiant light energy. Monte Carlo based integrators are the choice for complex scenes and effects. These integrators work by sampling the integrand at sample point locations. The distribution of these sample points determines convergence rates and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. Hence, it is essential to study these correlations to understand and adapt sample distributions for low error in integral approximation. In this work, we aim at providing a comprehensive and accessible overview of the techniques developed over the last decades to analyze such correlations, relate them to error in integrators, and understand when and how to use existing sampling algorithms for effective rendering workflows.
  • Thumbnail Image
    Item
    A Survey on Gradient-Domain Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hua, Binh-Son; Gruson, Adrien; Petitjean, Victor; Zwicker, Matthias; Nowrouzezahrai, Derek; Eisemann, Elmar; Hachisuka, Toshiya; Giachetti, Andrea and Rushmeyer, Holly
    Monte Carlo methods for physically-based light transport simulation are broadly adopted in the feature film production, animation and visual effects industries. These methods, however, often result in noisy images and have slow convergence. As such, improving the convergence of Monte Carlo rendering remains an important open problem. Gradient-domain light transport is a recent family of techniques that can accelerate Monte Carlo rendering by up to an order of magnitude, leveraging a gradient-based estimation and a reformulation of the rendering problem as an image reconstruction. This state of the art report comprehensively frames the fundamentals of gradient-domain rendering, as well as the pragmatic details behind practical gradient-domain uniand bidirectional path tracing and photon density estimation algorithms. Moreover, we discuss the various image reconstruction schemes that are crucial to accurate and stable gradient-domain rendering. Finally, we benchmark various gradient-domain techniques against the state-of-the-art in denoising methods before discussing open problems.