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Sample-Based Manifold Filtering for Interactive Global Illumination and Depth of Field.

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Title: Sample-Based Manifold Filtering for Interactive Global Illumination and Depth of Field.
Authors: Bauszat, P.1 bauszat@cg.cs.tu-bs.de, Eisemann, M.1 eisemann@cg.cs.tu-bs.de, John, S.1 john@quantic3d.com, Magnor, M.1 magnor@cg.cs.tu-bs.de
Source: Computer Graphics Forum. Feb2015, Vol. 34 Issue 1, p265-276. 12p.
Subjects: Manifolds (Mathematics), Interactive computer graphics, Global illumination algorithms, Signal filtering, Image reconstruction
Abstract: We present a fast reconstruction filtering method for images generated with Monte Carlo-based rendering techniques. Our approach specializes in reducing global illumination noise in the presence of depth-of-field effects at very low sampling rates and interactive frame rates. We employ edge-aware filtering in the sample space to locally improve outgoing radiance of each sample. The improved samples are then distributed in the image plane using a fast, linear manifold-based approach supporting very large circles of confusion. We evaluate our filter by applying it to several images containing noise caused by Monte Carlo-simulated global illumination, area light sources and depth of field. We show that our filter can efficiently denoise such images at interactive frame rates on current GPUs and with as few as 4-16 samples per pixel. Our method operates only on the colour and geometric sample information output of the initial rendering process. It does not make any assumptions on the underlying rendering technique and sampling strategy and can therefore be implemented completely as a post-process filter. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We present a fast reconstruction filtering method for images generated with Monte Carlo-based rendering techniques. Our approach specializes in reducing global illumination noise in the presence of depth-of-field effects at very low sampling rates and interactive frame rates. We employ edge-aware filtering in the sample space to locally improve outgoing radiance of each sample. The improved samples are then distributed in the image plane using a fast, linear manifold-based approach supporting very large circles of confusion. We evaluate our filter by applying it to several images containing noise caused by Monte Carlo-simulated global illumination, area light sources and depth of field. We show that our filter can efficiently denoise such images at interactive frame rates on current GPUs and with as few as 4-16 samples per pixel. Our method operates only on the colour and geometric sample information output of the initial rendering process. It does not make any assumptions on the underlying rendering technique and sampling strategy and can therefore be implemented completely as a post-process filter. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/cgf.12511
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      – Code: eng
        Text: English
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      – SubjectFull: Interactive computer graphics
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      – SubjectFull: Global illumination algorithms
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      – SubjectFull: Signal filtering
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      – SubjectFull: Image reconstruction
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      – TitleFull: Sample-Based Manifold Filtering for Interactive Global Illumination and Depth of Field.
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              Text: Feb2015
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