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A Layered Depth- of- Field Method for S olving Partial Occlus ion

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A Layered Depth- of- Field Method for S olving Partial Occlus ion

David C. Schedl Michael Wimmer

[email protected] [email protected]

Institute of Computer Graphics and Algorithms

Vienna University of Technology

WSCG – 26th June 2012

(2)

Real life partial occlus ion

David C. Schedl

f=18 mm, N=4

(3)

Intro/Overview

depth-of-field approximation

post processing

partial occlusion in realtime

(4)

Thin lens

David C. Schedl

(5)

Previous work

Potmesil and Chakravarty, 1981

CoC - equation

first post-processing method blur according to CoCs

still a reference artifacts

(6)

Artifacts

David C. Schedl

color bleeding:

[Demers2004]

[Riguer2005]

depth discontinuity:

(7)

Partial Occlus ion

pinhole vs. finite aperture

(8)

Partial Occlus ion

David C. Schedl

pinhole: finite aperture:

(9)

Previous work – s olve partial occlus ion

non-realtime:

ray-tracing (Cook et al., 1984)

Accumulation B. (Haeberli and Akeley, 1990)

layered methods:

Kraus and Strengert, 2007

occluded scene content only interpolated

Lee et al., 2010

image composition via ray traversal simulate more lens effects

more complex and slower than ours

(10)

Our Method

David C. Schedl

(11)

Our Method

(12)

Overview

David C. Schedl

(13)

Rendering & Depth Peeling

Images

(14)

Matting – functions

David C. Schedl

weight

depth

(15)

Matting – functions

weight

depth

(16)

Matting – layers

David C. Schedl

Input

(17)

Matting – layers

Input

(18)

Matting – layers

David C. Schedl

Input Layers

(19)

Matting – layers

Input Layers

(20)

Matting – layers

David C. Schedl

Input Layers

(21)

Blurring

uniformly blur layers Gaussian filter

(22)

Blurring

David C. Schedl

(23)

Blurring

(24)

Compos e

David C. Schedl

alpha-blend back to front

(25)

Optimization

reduce filter width recursive Gaussians

(26)

Optimization - front

David C. Schedl

(27)

Optimization - front

(28)

Optimization - front

David C. Schedl

(29)

Optimization - Compos iting

(30)

Res ults - Homunculus

David C. Schedl

f=100mm, N =1.4, focus=18 500 mm, 17 layers, 3x DP

(31)

Res ults - Dragons

f=100mm, N =1.4, focus=3 000 mm, 22 layers, 3x DP

(32)

Res ults – Benchmarks

David C. Schedl

Intel Core i7 920, Geforce GTX 480 OpenGL and GLSL

1024 x 1024px

ours Lee et. al. 2010 Accum. B.

optimized non-rec. 256 rays 256 views Homunculus

(74k tri.) 102 ms 1.4x 13.2x 47x

Dragons

(610k tri.) 98 ms 1.3x 14.7x 42x

(33)

Conclus ion

layered DoF method

partial occlusion solved comparison to:

Accumulation Buffer Lee et al., 2010

optimized by recursive Gaussians

efficient composition with alpha blending

(34)

Outlook

David C. Schedl

screen-spaced antialiasing

avoid empty layers: clustering

inaccurate but faster blurring methods combine with eye-tracker

(35)

Thank you!

slides will be available at:

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