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Using GenProg to Improve Computer Graphics. Adam Brady University of Virginia. 1988 . 1988 2009. Realistic materials are hard. Project Goal: How can we better model real materials in computer graphics?

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Using genprog to improve computer graphics

Using GenProg to Improve Computer Graphics

Adam Brady

University of Virginia




Realistic materials are hard
Realistic materials are hard

Project Goal: How can we better model real materials in computer graphics?

Current modeling techniques fall under two distinct strategies


Strategy 1 math
Strategy 1: Math

Step 1) Think hard about physics


Strategy 1 math1
Strategy 1: Math

Step 1) Think hard about physics

Step 2) Channel James Clerk Maxwell


Strategy 1 math2
Strategy 1: Math

Step 1) Think hard about physics

Step 2) Channel James Clerk Maxwell

Step 3) Write down an equation that

approximates the physical world


Important considerations
Important Considerations

What sort of patterns exist in the microscopic geometry structure?

Can we statistically model light scattering?

VERY FEW of these functions exist


Problems with this strategy
Problems with This Strategy

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:


Problems with this strategy1
Problems with This Strategy

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:


Strategy 2 measurement
Strategy 2: Measurement

Step 1) Acquire a sample of a real material


Strategy 2 measurement1
Strategy 2: Measurement

Step 1) Acquire a sample of a real material

Step 2) Capture reflectance byphysically

moving a light and camera


Strategy 2 measurement2
Strategy 2: Measurement

Step 1) Acquire a sample of a real material

Step 2) Capture reflectance byphysically

moving a light and camera

Step 3) Record data in one massive table


Problems with this strategy2
Problems with This Strategy

  • The table is HUGE

    • Millions of samples needed

  • No fundamental truths gained

    • Data only describes one specific material


We need a better way
We Need a Better Way

  • Math expressions are compact and contain adjustable parameters…

    • But inaccurate

  • Measured data is accurate..

    • But HUGE and static


Project outline
Project Outline

  • Use GenProgto modify reflectance functions

    • (Think small math expressions written in C)


Project outline1
Project Outline

  • Use GenProg to modify reflectance functions

    • (Think small math expressions written in C)

  • Measure fitness by rendering an image using the new function

    • Compare to reference image produced using measured data


Project outline2
Project Outline

  • Use GenProg to modify reflectance functions

    • (Think small math expressions written in C)

  • Measure fitness by rendering an image using the new function

    • Compare to reference image produced using measured data

  • Goal: Achieve measured data accuracy using a compact, adjustable math expression


INPUT

EVALUATE FITNESS

DISCARD

ACCEPT

OUTPUT

MUTATE


INPUT

Error: 0.4523


INPUT

Error: 0.4523

MUTATE


INPUT

EVALUATE FITNESS

Compare

Error: 0.4523

Error: ?

MUTATE


INPUT

EVALUATE FITNESS

Compare

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

MUTATE


INPUT

EVALUATE FITNESS

Compare

Error: 0.9702

DISCARD

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

MUTATE


INPUT

EVALUATE FITNESS

Compare

Error: 0.9702

DISCARD

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

Error: 0.012

OUTPUT

MUTATE


Key differences from genprog
Key Differences from GenProg

  • Optimize for visual similarity rather than pass/fail correctness

    • Our fitness evaluation is continuous rather than discrete


Key differences from genprog1
Key Differences from GenProg

  • Optimize for visual similarity rather than pass/fail correctness

    • Our fitness evaluation is continuous rather than discrete

  • No fault localization

    • Entire C program can be changed


Key differences from genprog2
Key Differences from GenProg

  • We typically construct entirely new programs rather than make minor edits


Key differences from genprog3
Key Differences from GenProg

  • We typically construct entirely new programs rather than make minor edits

  • We use additional domain-specific mutation operators that operateat the expression level

    • sin(x+y) cos(x+y), vectora vectorb, etc.


Experiment setup
Experiment Setup

Learn a new function for a single material

32 generations, 2048 functions/generation



Results1
Results

“I wouldn’t have thought of that!”

-UVa Graphics Researcher


Current work
Current Work

Results only consider a singlematerial

Learning functions that fit multiple materials is much more difficult


Conclusions
Conclusions

We used GenProglearn new compact, elegant expressions to accurately describe real materials

However, additional work needs to be done to learn more general models



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