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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Veröffentlicht in:Computer graphics forum Jg. 34; H. 1; S. 265 - 276
Hauptverfasser: Bauszat, P., Eisemann, M., John, S., Magnor, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.02.2015
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
Bibliographie:ark:/67375/WNG-8NS3CZ18-N
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ArticleID:CGF12511
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12511