Title: Geometry Images
1Geometry Images
Steven Gortler Harvard University
Xianfeng Gu Harvard University
Hugues Hoppe Microsoft Research
2Irregular meshes
Vertex 1 x1 y1 z1 Vertex 2 x2 y2 z2
Face 2 1 3 Face 4 2 3
3Texture mapping
Vertex 1 x1 y1 z1 Vertex 2 x2 y2 z2
s1 t1 s2 t2
Face 2 1 3 Face 4 2 3
t
normal map
s
4Complicated rendering process
Vertex 1 x1 y1 z1 Vertex 2 x2 y2 z2
s1 t1 s2 t2
Face 2 1 3 Face 4 2 3
random access!
random access!
40M ?/sec
5Semi-regular representations
Eck et al 1995 Lee et al 1998 Khodakovsky
2000 Guskov et al 2000
only semi-regular
irregular vertex indices
6Geometry Image
3D geometry
completely regular sampling
geometry image257 x 257 12 bits/channel
7Basic idea
cut
parametrize
demo
8Basic idea
cut
sample
9Basic idea
cut
store
render
r,g,b x,y,z
10How to cut ?
2D surface disk
sphere in 3D
11How to cut ?
2D surface disk
sphere in 3D
- Genus-0 surface ? any tree of edges
12How to cut ?
torus (genus 1)
- Genus-g surface ? 2g generator loops minimum
13Surface cutting algorithm
- (1) Find topologically-sufficient cut
- 2g loops Dey and Schipper 1995
Erickson and Har-Peled 2002 - (2) Allow better parametrization
- additional cut paths Sheffer 2002
14Step 1 Find topologically-sufficient cut
(a) retract 2-simplices
(b) retract 1-simplices
15Results of Step 1
genus 6
genus 0
genus 3
16Step 2 Augment cut
- Make the cut pass through extrema (note not
local phenomena). - Approach parametrize and look for bad areas.
17Step 2 Augment cut
iterate while parametrization improves
18Results of Steps 1 2
genus 1
genus 0
19Parametrize boundary
a
a
a
a
- Constraints
- cut-path mates identical length
- endpoints at grid points
? no cracks
20Parametrize interior
- Geometric-stretch metric
- minimizes undersampling Sander et al 2001
- optimizes point-sampled approx. Sander et al
2002
21Stretch parametrization
Previous metrics
(Floater, harmonic, uniform, )
22Sample
geometry image
23Rendering
(65x65 geometry image)
24Rendering with attributes
geometry image 2572 x 12b/ch
normal-map image 5122 x 8b/ch
rendering
25Advantages for hardware rendering
- Regular sampling ? no vertex indices.
- Unified parametrization ? no texture
coordinates. - ? Raster-scan traversal of source data
geometry attribute samples in lockstep. - Summary compact, regular, no indirection
26Normal-Mapped Demo
geometry image129x129 12b/ch
normal map512x512 8b/ch
demo
27Pre-shaded Demo
geometry image129x129 12b/ch
color map512x512 8b/ch
demo
28Results
257x257
normal-map 512x512
29Results
257x257
color image 512x512
30Mip-mapping
257x257
129x129
65x65
31Hierarchical culling
view-frustum culling
geometry image
backface culling
normal-map image
32Compression
Image wavelet-coder
? 1.5 KB
295 KB
fused cut
topological sideband (12 B)
33Compression results
295 KB ?
1.5 KB
3 KB
12 KB
49 KB
34Rate distortion
35Some artifacts
aliasing
anisotropic sampling
36Summary
- Simple rendering compact, no indirection,
raster-scan stream. - Mipmapped geometry
- Hierarchical culling
- Compressible
37Future work
- Better cutting algorithms
- Feature-sensitive remeshing
- Tangent-frame compression
- Bilinear and bicubic rendering
- Build hardware
38(No Transcript)