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## PowerPoint Slideshow about ' Using GenProg to Improve Computer Graphics' - herbst

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Presentation Transcript

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

Step 1) Think hard about physics

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

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

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:

Problems with This Strategy

Finding fundamental approximations of the natural world is hard

Assumptions limit accuracy:

Strategy 2: Measurement

Step 1) Acquire a sample of a real material

Strategy 2: Measurement

Step 1) Acquire a sample of a real material

Step 2) Capture reflectance byphysically

moving a light and camera

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 Strategy

- The table is HUGE
- Millions of samples needed
- No fundamental truths gained
- Data only describes one specific material

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

- Use GenProgto modify reflectance functions
- (Think small math expressions written in C)

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 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

Error: 0.4523

EVALUATE FITNESS

Compare

Error: 0.9702

DISCARD

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.

Current Work

Results only consider a singlematerial

Learning functions that fit multiple materials is much more difficult

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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