feature sensitive bas relief generation

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Feature Sensitive Bas Relief Generation Jens Kerber 1 , Art Tevs 1 , Alexander Belyaev 2 , Rhaleb Zayer 3 , and Hans-Peter Seidel 1 1 Max-Planck-Instut für Informatik, Saarbrücken 2 Joint Research Institute for Image and Signal Processing, Edinburgh 3 LORIA-INRIA Loraine, CNRS, Nancy

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Feature Sensitive Bas Relief Generation. Jens Kerber 1 , Art Tevs 1 , Alexander Belyaev 2 , Rhaleb Zayer 3 , and Hans-Peter Seidel 1 1 Max-Planck- Instut f ü r Informatik, Saarbr ü cken 2 Joint Research Institute for Image and Signal Processing, Edinburgh - PowerPoint PPT Presentation

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Page 1: Feature Sensitive Bas Relief Generation

Feature Sensitive Bas Relief Generation

Jens Kerber 1, Art Tevs 1, Alexander Belyaev 2,Rhaleb Zayer 3, and Hans-Peter Seidel 1

1 Max-Planck-Instut für Informatik, Saarbrücken2 Joint Research Institute for Image and Signal Processing, Edinburgh

3 LORIA-INRIA Loraine, CNRS, Nancy

Page 2: Feature Sensitive Bas Relief Generation

Motivation Aim

– Compress depth-interval size of height field– No loss of important features

Applications for Bas-Reliefs– Coinage– Packaging– Shape Decoration

Embossment Engraving Carving

– Displacement Maps

SMI 2009, Tsinghua University, Beijing, China1 http://www.cachecoins.org/2 Real-time relief mapping on arbitrary polygonal surfaces Policarpo F., Oliveira M., Comba J. L. D., SIGGRAPH 2005

1

2

Page 3: Feature Sensitive Bas Relief Generation

SMI 2009, Tsinghua University, Beijing, China

Naïve Approach Linear Rescaling

Page 4: Feature Sensitive Bas Relief Generation

Related Work Automatic generation of bas-reliefs from 3D shapes

W. Song, A. Belyaev, H.-P. Seidel, SMI 2007 (short paper)+ Introducing the problem and attempting to solve it

Digital Bas-Relief from 3D ScenesT. Weyrich, J. Deng, C. Barnes, S. Rusinkiewicz, A. Finkelstein, SIGGRAPH 2007

+ Impressive results - Much user interaction required, computationally expensive

Feature Preserving Depth Compression of Range ImagesJ. Kerber, A. Belyaev, H.-P. Seidel, SCCG 2007

+ Simple and fast - Spherical parts not well reproduced, problems with noise

Bas-Relief Generation Using Adaptive Histogram EqualizationX. Sun, P. Rosin, R. Martin, TVCG 2009

+ Very good results - Time consuming

SMI 2009, Tsinghua University, Beijing, China

Page 5: Feature Sensitive Bas Relief Generation

Pipeline

Gradient Extraction

Silhouette Removal

OutlierDetection

Attenuation

Decomposition

Re-assemblin

gRescaling Re-

weighting

SMI 2009, Tsinghua University, Beijing, China

I

R

Page 6: Feature Sensitive Bas Relief Generation

SMI 2009, Tsinghua University, Beijing, China

Silhouette Treatment Gradient of the Background mask = 1 ?

Page 7: Feature Sensitive Bas Relief Generation

Outlier Detection Tollerance parameter

– Deviation to mean gradient value

SMI 2009, Tsinghua University, Beijing, China

Page 8: Feature Sensitive Bas Relief Generation

Signal Decomposition

Base-layer and Detail-layer Detail Enhancement Base Compression

SMI 2009, Tsinghua University, Beijing, China

DetailBase1

Page 9: Feature Sensitive Bas Relief Generation

Gradient domain Edge preservation

Gradient extrema preservation

Spatial Domain Preservation of ridges

and valleys

Curvature extrema preservation

SMI 2009, Tsinghua University, Beijing, China

Bilateral Filter

Page 10: Feature Sensitive Bas Relief Generation

Reweighting

SMI 2009, Tsinghua University, Beijing, China

Before After

X-Gradient

Y-Gradient

Page 11: Feature Sensitive Bas Relief Generation

SMI 2009, Tsinghua University, Beijing, China

Poisson Reconstruction Given Ix, Iy

Compute Ixx + Iyy = ΔI

Partial Differential Equation

Well studied Problem

Multi-Grid-Solver– Assumption: Frame equals background

Page 12: Feature Sensitive Bas Relief Generation

Results

SMI 2009, Tsinghua University, Beijing, China

Page 13: Feature Sensitive Bas Relief Generation

Cubism Ancient technique in art Combine multiple viewpoints in a single painting

Aim: extend this effect from 2D to sculpting

SMI 2009, Tsinghua University, Beijing, China 3 http://picasso.tamu.edu/picasso/

3 3

Page 14: Feature Sensitive Bas Relief Generation

Height Field Capturing

Open GL Application 180˚ in 15˚ steps Composition in 2D

SMI 2009, Tsinghua University, Beijing, China

0

-30

30 75

-75

Page 15: Feature Sensitive Bas Relief Generation

Transition Problems Transition areas

– Seams are automatically detected as outliers– But set to 0– Flat transitions would emphazise the impression

of having two different parts

SMI 2009, Tsinghua University, Beijing, China

Page 16: Feature Sensitive Bas Relief Generation

Transition Problems (ctd.) 0-Gradient affects 3x3 neighborhood Re-fill affected area Weighted average (Gauss) excluding masked entries

Seamless results

SMI 2009, Tsinghua University, Beijing, China

Page 17: Feature Sensitive Bas Relief Generation

Results

SMI 2009, Tsinghua University, Beijing, China

Page 18: Feature Sensitive Bas Relief Generation

Performance

SMI 2009, Tsinghua University, Beijing, China

Intel 4x2.6 GHz, 8GB, Matlab64 Implementation Bottleneck

– Bilateral Filter– Poisson Reconstruction

Not optimized yet, possible acceleration

Model Resolution / pixel Time / secondsLucy 950x800 6.2

Lion-Vase 950x800 6.8

XYZRGB Dragon 980x1700 13.5

David Cubism 1 1200x1200 17.2

David Cubism 2 800x800 8.1

Page 19: Feature Sensitive Bas Relief Generation

Conclusion

Contribution– Little user intervention– Preservation of fine and sharp structural details– More artistic freedom– Potentially Fast– Independent of complexity– Commercial applications

SMI 2009, Tsinghua University, Beijing, China

Page 20: Feature Sensitive Bas Relief Generation

SMI 2009, Tsinghua University, Beijing, China

Future work Dynamic extension

– Video

Thank you for your attention!