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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 f ü r Informatik, Saarbr ü cken 2 Joint Research Institute for Image and Signal Processing, Edinburgh

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Feature Sensitive Bas Relief Generation

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  1. Feature Sensitive Bas Relief Generation Jens Kerber1, Art Tevs1, Alexander Belyaev2, RhalebZayer3, andHans-Peter Seidel1 1 Max-Planck-Instut für Informatik, Saarbrücken 2 Joint Research Institute for Image and Signal Processing, Edinburgh 3 LORIA-INRIA Loraine, CNRS, Nancy

  2. 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 • DisplacementMaps 1 2 1 http://www.cachecoins.org/ 2Real-time relief mapping on arbitrary polygonal surfaces Policarpo F., Oliveira M., Comba J. L. D., SIGGRAPH 2005 SMI 2009, Tsinghua University, Beijing, China

  3. Naïve Approach • Linear Rescaling SMI 2009, Tsinghua University, Beijing, China

  4. Related Work • Automatic generation of bas-reliefs from 3D shapesW. Song, A. Belyaev, H.-P. Seidel, SMI 2007 (short paper) + Introducing the problem and attempting to solve it • Digital Bas-Relief from 3D Scenes T. 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 Images J. 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 Equalization X. Sun, P. Rosin, R. Martin, TVCG 2009 + Very good results - Time consuming SMI 2009, Tsinghua University, Beijing, China

  5. Pipeline Outlier Detection Gradient Extraction Silhouette Removal Attenuation I Rescaling Re-assembling Re-weighting Decompo sition R SMI 2009, Tsinghua University, Beijing, China

  6. Silhouette Treatment • Gradient of the Background mask = 1 ? SMI 2009, Tsinghua University, Beijing, China

  7. Outlier Detection • Tollerance parameter • Deviation to mean gradient value SMI 2009, Tsinghua University, Beijing, China

  8. Signal Decomposition • Base-layer and Detail-layer • Detail Enhancement • Base Compression SMI 2009, Tsinghua University, Beijing, China

  9. Bilateral Filter Gradient domain Spatial Domain Preservation of ridges and valleys Curvature extrema preservation • Edge preservation • Gradient extrema preservation SMI 2009, Tsinghua University, Beijing, China

  10. Reweighting Before After • X-Gradient • Y-Gradient SMI 2009, Tsinghua University, Beijing, China

  11. PoissonReconstruction • Given Ix, Iy • Compute Ixx + Iyy = ΔI • Partial Differential Equation • Well studied Problem • Multi-Grid-Solver • Assumption: Frame equals background SMI 2009, Tsinghua University, Beijing, China

  12. Results SMI 2009, Tsinghua University, Beijing, China

  13. Cubism • Ancient technique in art • Combine multiple viewpoints in a single painting • Aim: extend this effect from 2D to sculpting 3 3 SMI 2009, Tsinghua University, Beijing, China 3 http://picasso.tamu.edu/picasso/

  14. Height Field Capturing • Open GL Application • 180˚ in 15˚ steps • Composition in 2D 30 75 0 -75 -30 SMI 2009, Tsinghua University, Beijing, China

  15. 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

  16. 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

  17. Results SMI 2009, Tsinghua University, Beijing, China

  18. Performance • Intel 4x2.6 GHz, 8GB, Matlab64 Implementation • Bottleneck • Bilateral Filter • Poisson Reconstruction • Not optimized yet, possible acceleration SMI 2009, Tsinghua University, Beijing, China

  19. Conclusion • Contribution • Little userintervention • Preservation of fine and sharp structural details • More artisticfreedom • Potentially Fast • Independent of complexity • Commercial applications SMI 2009, Tsinghua University, Beijing, China

  20. Future work • Dynamic extension • Video • Thank you for your attention! SMI 2009, Tsinghua University, Beijing, China

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