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

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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

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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

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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

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Depth-of-Field

• More naturally looking images• Important depth cue for perception of scene

configuration• Draws attention to the focused objects

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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

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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

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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

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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

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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

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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

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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)

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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

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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

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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]

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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

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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

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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

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Results

No Depth-of-Field – everything in focus

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Results

Transparent mask in focus, male figure out of focus

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Results

Male figure in focus, transparent mask out of focus

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Results

Our algorithm Reference solution

(multisampling)

• Our blur looks too smooth because of the Gaussian filter

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Results

Our algorithm Reference solution

(multisampling)

• Artifacts due to incorrect surface reconstruction

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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

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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