Loading in 2 Seconds...

A Self Shadow Algorithm for Dynamic Hair using Density Clustering

Loading in 2 Seconds...

- By
**shae** - Follow User

- 85 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' A Self Shadow Algorithm for Dynamic Hair using Density Clustering' - shae

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### A Self Shadow Algorithm for Dynamic Hair using Density Clustering

Tom Mertens1 Jan Kautz2 Philippe Bekaert1 Frank Van Reeth1

Limburgs Universitair Centrum - Belgium1 MIT – Cambridge, MA2

Rendering Hair

- Realistic appearance
- Light Scattering

[Marschner et al ’03]

- Multiple Scattering
- Self-shadows
- Dynamic hair, real-time rate

GPU

Without self-shadows

With self-shadows

Self-Shadows for Hair

- Canonical solutions
- Shadow Volume [Crow ’77]
- Shadow Mapping [Williams ’78]

Self-Shadows for Hair

- Canonical solutions
- Shadow Volume [Crow ’77]
- Shadow Mapping [Williams ’78]
- Problems
- Geometric complexity
- Hair strand ≤ pixel

→ aliasing

Shadow Mapping

light source view

depth( )

shadow map

depth( ) > depth( ) shadowed

[Williams et al. ’78]

Opacity Shadow Maps

1D visibility function

Near interactive

performance

Regular sampling

[Kim et al. ’01]

Images from [Kim et al. ‘01]

Density Clustering

- Density estimation

histogram

s

s

Our approach: non-uniform

- Uniform
- Opacity Shadow Mapping

[Kim et al. ’01]

Density Clustering

Rasterize hair strands

Density Clustering

d0 d1 d2 … dn

β0β1β2 … βn

Clustering of di’s

K-means

(while rasterizing)

- Pick initial clusters
- Update clusters
- get closest samples
- new cluster pos
- = mean of closest

Density Clustering

d0 d1 d2 … dn

β0β1β2 … βn

Clustering of di’s

K-means

(while rasterizing)

- Pick initial clusters
- Update clusters
- get closest samples
- new cluster pos
- = mean of closest

Density Clustering

d0 d1 d2 … dn

β0β1β2 … βn

Clustering of di’s

K-means

(while rasterizing)

- Pick initial clusters
- Update clusters
- get closest samples
- new cluster pos
- = mean of closest

Density Clustering

d0 d1 d2 … dn

β0β1β2 … βn

Clustering of di’s

K-means

(while rasterizing)

Histogram bins

μ = mean

σ = standard dev

μ

μ

~σ

~σ

Rendering Overview

- For light source view
- Compute initial clusters
- Rasterize K-means
- Construct bins
- Fill histogram bins
- Integrate σt

ò

s

dt

t

0

Rendering Overview- For light source view
- Compute initial clusters
- Rasterize K-means
- Construct bins
- Fill histogram bins
- Integrate σt

- Eye view: shadow attenuation
- Use piecewise linear function as lookup

Conceptual Comparison

all intersections

along ray

Ground truth

Uniform (8-bins)

Non-uniform (4-means clustering)

Conclusion

- Realistic self-shadowing
- Interactive rates
- 200K line segments @ 3Hz (scales linearly)
- Order of magnitude speedup Opacity Sh. Maps

Conclusion

- Realistic self-shadowing
- Interactive rates
- 200K line segments @ 3Hz (scales linearly)
- Order of magnitude speedup Opacity Sh. Maps
- Future work
- Implementation on next generation HW

4x speedup expected (due to floating point blending)

- Better initial clusters for K-means
- Explore other cluster techniques
- Smoke, clouds, …

Thank you for your attention

The first author gratefully acknowledges the

European Commission (European Regional Development Fund).

Qualitative Comparison

N=256

N=64

N=16

N=4

Opacity Shadow Mapping

[Kim et al. ’01]

(N slices)

Our method

(4 clusters)

Qualitative Comparison

N=256

N=64

N=16

N=4

Opacity Shadow Mapping

[Kim et al. ’01]

(N slices)

Our method

(4 clusters)

Qualitative Comparison

N=256

N=64

N=16

N=4

Opacity Shadow Mapping

[Kim et al. ’01]

(N slices)

Our method

(4 clusters)

Download Presentation

Connecting to Server..