A Survey on Gradient-Domain Rendering

Binh Son Hua, Adrien Gruson, Victor Petitjean, Matthias Zwicker, Derek Nowrouzezahrai, Elmar Eisemann, Toshiya Hachisuka

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)455-472
Number of pages18
JournalComputer Graphics Forum
Issue number2
Publication statusPublished - 1 May 2019


  • CCS Concepts
  • •Computing methodologies → Ray tracing


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