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

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 fi...

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Vydáno v:	Computer graphics forum Ročník 34; číslo 1; s. 265 - 276
Hlavní autoři:	Bauszat, P., Eisemann, M., John, S., Magnor, M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.02.2015
Témata:	Analysis Computer graphics Computer simulation Depth of field Filtering Filtration Frames global illumination I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism > -Raytracing I.4.3 [Computer Graphics]: Enhancement > -Filtering Illumination Image processing systems Image reconstruction Interactive Light sources Monte Carlo simulation Noise Noise reduction Radiance ray tracing real-time renderingACM CCS: I.3.6 [Computer Graphics]: Methodology and Techniques > -Graphics data structures and data types real‐time rendering ACM CCS: I.3.6 [Computer Graphics]: Methodology and Techniques‐‐‐Graphics data structures and data types; I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism‐‐‐Raytracing; I.4.3 [Computer Graphics]: Enhancement‐‐‐Filtering Rendering Sampling Studies
ISSN:	0167-7055, 1467-8659
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Shrnutí:	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. 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 spp.
Bibliografie:	ark:/67375/WNG-8NS3CZ18-N Video S1 istex:96DCA4D26BE0144EBFEBAE18CB734754FB7BCEDA ArticleID:CGF12511 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0167-7055 1467-8659
DOI:	10.1111/cgf.12511