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A Survey on Gradient‐Domain Rendering
Author(s) -
Hua BinhSon,
Gruson Adrien,
Petitjean Victor,
Zwicker Matthias,
Nowrouzezahrai Derek,
Eisemann Elmar,
Hachisuka Toshiya
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13652
Subject(s) - rendering (computer graphics) , path tracing , computer science , global illumination , monte carlo method , ray tracing (physics) , computer vision , artificial intelligence , 3d rendering , computer graphics (images) , algorithm , mathematics , statistics , physics , quantum mechanics
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.