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Fast Depth-of-Field Rendering with Surface Splatting. Jaroslav K ř ivánek CTU Prague IRISA – INRIA Rennes. Ji ř í Žára CTU Prague. Kadi Bouatouch IRISA – INRIA Rennes. Computer. Graphics. Group. Goal. Depth-of-field rendering with point-based objects Why point-based ? - PowerPoint PPT Presentation
Citation preview
Fast Depth-of-Field Rendering with Surface
Splatting
Jaroslav Křivánek
CTU PragueIRISA – INRIA Rennes
Jiří Žára
CTU Prague
Kadi Bouatouch
IRISA – INRIA Rennes
ComputerGraphics Group
2/25
Goal
• Depth-of-field rendering with point-based objects
• Why point-based ?– Efficient for complex objects
• Why depth-of-field ?– Nice and naturally looking images
3/25
Overview
• Introduction – Point-based rendering– Depth-of-field
• Depth-of-field techniques• Our contribution: Point-based depth-of-field
rendering– Basic approach– Extended method: depth-of-field with level of detail
• Results• Discussion• Conclusions
4/25
Point-based rendering
• Object represented by points without connectivity
• Point (surfel) – position, normal,
radius, material
• Rendering = screen space surface reconstruction
• Efficient for very complex objects
x
yz
5/25
Depth-of-Field
• More naturally looking images• Important depth cue for perception of scene
configuration• Draws attention to the focused objects
6/25
Thin Lens Camera Model
image plane focal planelens
VP P
F/n
DVD
C
Circle of Confusion (CoC)
C = f ( F, F/n, D, P )
F…... focal distanceF/n… lens diameterP……focal plane distanceD……point depth
7/25
Depth-of-Field Techniques in CG
• Supersampling– Distributed ray tracing [Cook et al. 1984]– Sample the light paths through the lens
• Multisampling [Haeberli & Akeley 1990]– Several images from different viewpoints on the
lens– Average the resulting images using accumulation
buffer
8/25
Depth of Field Techniques in CG
• Post-filtering [Potmesil & Chakravarty 1981]– Out-of-focus pixels displayed as CoC– Intensity leakage, hypo-intensity– Slow for larger kernels
Focus processor(filtering)
Image + depth Image with DOF
Image synthesizer
9/25
Point-based rendering - splatting• Draw each point as a fuzzy splat (an ellipse)
Image = SPLATi
o b jec t sp ace
x
yz
sc reen sp ace
x
y
splat
10/25
Our Basic Approach
• Post-filtering
Focus processor(filtering)Image + depth
Image with DOF
Image =i SPLATi
i SPLATi + depth
• Our Approach: Swap and Focus filteringFocus filtering
Image with DOF
SPLATi
Focus filteringSPLATj
Focus filteringSPLATk
11/25
Our Basic Approach
o b jec t sp ace
x
yz
sc reen sp ace
x
y
Splat = reconstr. kernel
DOF filter GQDOF
Blurred reconstr.
kernel
DOFGQDOF
12/25
Properties of our basic approachPROS…+Avoids leakage
– Reconstruction takes into account the splat depth
+No hypo-intensities– Visibility resolved after blurring
+Handles transparency– In the same way as the EWA splatting – A-buffer
CONS- Very slow, especially for large apertures
– A lot of large overlapping splats– High number of fragments:
• E.g. Lion, no blur: 2.3 mil.; blur 90.2 mil. (40x more)
13/25
Our Extended Method
• Use Level of Detail (LOD) to attack complexity
• blur = detail• Select lower LOD for blurred parts
• # of fragments increases more slowly• E.g. Lion, no blur: 2.3 mil.; blur 5.3 mil. (2.3x
more)
Blurred img. Selected LOD
14/25
Fine LOD Lower LOD
Observation
• Selecting lower LOD for rendering equivalent to 1) selecting the fine LOD 2) low-pass filtering is screen space
• Use LOD as a means for blurring – not only to reduce complexity
15/25
Effect of LOD Selection
• How to quantify the effect of LOD selection in
terms of blur in the resulting image ?
• We use Bounding sphere hierarchy – Qsplat [Rusinkiewicz & Levoy, 2000]
16/25
Bounding Sphere Hierarchy
The finest level: L=0 Lower level: L=1
subsample
• Building the hierarchy levels low-pass filtering + subsampling
Center the filter GQL
17/25
LOD Filter in Screen Space
• GQL defined in local coordinates in object space
• GQL related to screen space by the local affine approximation J of the object-to-screen transform
• Selecting level L = filtering in screen space by GJQLJT
Screen space
GQLGJQLJT
Object space
18/25
DOF with LOD - Algorithm
• Given the required screen space filter GQDOF 1. Select LOD L such that
support( GJQLJT ) < support ( GQDOF )
2. Apply an additional screen space filter GQDIFF to get GQDOF o b jec t sp ace
x
yz
x
y
DOFGQDOFDOFGJQLJTGQDIFF
GJQLJT
19/25
Results
No Depth-of-Field – everything in focus
20/25
Results
Transparent mask in focus, male figure out of focus
21/25
Results
Male figure in focus, transparent mask out of focus
22/25
Results
Our algorithm Reference solution
(multisampling)
• Our blur looks too smooth because of the Gaussian filter
23/25
Results
Our algorithm Reference solution
(multisampling)
• Artifacts due to incorrect surface reconstruction
24/25
Discussion
• Simplifying assumptions & limitations– Gaussian distribution of light within the CoC
• Mostly ok– We are blurring the texture before lighting
• We should blur after lighting– Possible incorrect image reconstruction from
blurred splats
25/25
Conclusion
• A novel algorithm for depth of field rendering• LOD as a means for depth-blurring+ Transparency+ Avoids intensity leakage+ Running time independent of the DOF- Only for point based rendering- A number of artifacts can appear• Ideal tool for interactive DOF previewing
– Trial and error camera parameters setting
Acknowledgement: Grant 2159/2002 MSMT Czech Republic