A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo rendering

A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo... Although many adaptive sampling and reconstruction techniques for Monte Carlo (MC) rendering have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on to those available for that renderer. In this paper, we propose a renderer-agnostic framework for testing and benchmarking sampling and denoising techniques for MC rendering, which allows an algorithm to be easily deployed to multiple rendering systems and tested on a wide variety of scenes. Our system achieves this by decoupling the techniques from the rendering systems, hiding the renderer details behind an API. This improves productivity and allows for direct comparisons among techniques originally developed for different rendering systems. We demonstrate the effectiveness of our API by using it to instrument four rendering systems and then using them to benchmark several state-of-the-art MC denoising techniques and sampling strategies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo rendering

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-018-1521-y
Publisher site
See Article on Publisher Site

Abstract

Although many adaptive sampling and reconstruction techniques for Monte Carlo (MC) rendering have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on to those available for that renderer. In this paper, we propose a renderer-agnostic framework for testing and benchmarking sampling and denoising techniques for MC rendering, which allows an algorithm to be easily deployed to multiple rendering systems and tested on a wide variety of scenes. Our system achieves this by decoupling the techniques from the rendering systems, hiding the renderer details behind an API. This improves productivity and allows for direct comparisons among techniques originally developed for different rendering systems. We demonstrate the effectiveness of our API by using it to instrument four rendering systems and then using them to benchmark several state-of-the-art MC denoising techniques and sampling strategies.

Journal

The Visual ComputerSpringer Journals

Published: May 2, 2018

References

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