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Using GenProg to Improve Computer Graphics - PowerPoint PPT Presentation

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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Presentation Transcript

Using GenProg to Improve Computer Graphics

University of Virginia

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

Current modeling techniques fall under two distinct strategies

Step 1) Think hard about physics

Step 1) Think hard about physics

Step 2) Channel James Clerk Maxwell

Step 1) Think hard about physics

Step 2) Channel James Clerk Maxwell

Step 3) Write down an equation that

approximates the physical world

What sort of patterns exist in the microscopic geometry structure?

Can we statistically model light scattering?

VERY FEW of these functions exist

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:

Step 1) Acquire a sample of a real material

Step 1) Acquire a sample of a real material

Step 2) Capture reflectance byphysically

moving a light and camera

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

• The table is HUGE

• Millions of samples needed

• No fundamental truths gained

• Data only describes one specific material

• Math expressions are compact and contain adjustable parameters…

• But inaccurate

• Measured data is accurate..

• But HUGE and static

• Use GenProgto modify reflectance functions

• (Think small math expressions written in C)

• 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

• 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

EVALUATE FITNESS

ACCEPT

OUTPUT

MUTATE

Error: 0.4523

Error: 0.4523

MUTATE

EVALUATE FITNESS

Compare

Error: 0.4523

Error: ?

MUTATE

EVALUATE FITNESS

Compare

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

MUTATE

EVALUATE FITNESS

Compare

Error: 0.9702

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

MUTATE

EVALUATE FITNESS

Compare

Error: 0.9702

ACCEPT

Error: 0.4523

Error: ?

Error: 0.3201

Error: 0.012

OUTPUT

MUTATE

Key Differences from GenProg

• Optimize for visual similarity rather than pass/fail correctness

• Our fitness evaluation is continuous rather than discrete

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 GenProg

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

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.

Learn a new function for a single material

32 generations, 2048 functions/generation

“I wouldn’t have thought of that!”

-UVa Graphics Researcher

Results only consider a singlematerial

Learning functions that fit multiple materials is much more difficult

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