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## Wavelets on Surfaces

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**Wavelets on Surfaces**In partial fulfillment of the “Area Exam” doctoral requirements By Samson Timoner May 8, 2002 (picture from “Wavelets on Irregular Point Sets”)**Papers**• Wavelets on Irregular Point Setsby Daubechies Guskov, Schroder and Sweldens(Trans. R. Soc. 1999) • “Spherical Wavelets: Efficiently Representation Functions on the Sphere” by Schroder and Sweldens • “The Lifting Scheme: Construction of second generation wavelets” by Sweldens • Multiresolution Signal Processing For Meshesby Guskov, Sweldens and Schroder(Siggraph 1999) • Multiresolution Hierarchies On Unstructured Triangle Meshes by Kobbelt, Vorsatz, and Seidel (Compu. Geometry: Theory and Applications, 1999)**Outline**• Wavelets • The Lifting Scheme • Extending the Lifting Scheme • Application: Wavelets on Spheres • Wavelets on Triangulated Surfaces • Applications**Wavelets**• Multi-resolution representation. • Basis functions (low pass filter). • Detail Coefficients (high pass filter). • We have bi-orthogonality between the detail coefficients and the basis-coefficients • Vanishing Moments**The Lifting Scheme**Split**The Lifting Scheme**Predict**The Lifting Scheme**Predict**The Lifting Scheme**Update 1/8{-1,2,6,2,-1}, ½{-1,2,-1}**The Lifting Scheme**• Introduced “Prediction” • Translated and Scaled one filter. • We have bi-orthogonality between the detail coefficients and the basis-coefficients • 2 Vanishing Moments (mean and first) More Details**Irregularly Sampled Points**Split Predict Update**Irregularly Sampled Points**• Filters are no longer translations of each other. • Detail coefficients indicate different frequencies. • Perhaps it is wiser not to select every other point? • You can show bi-orthogonality(by vanishing moments).**Wavelets on Spheres**• Sub-division on edges • Same steps • Split • Predict • Update**Topological Earth Data**• Data is not smooth • All bases performed equally poorly. (picture from “Spherical Wavelets”) 15,000 coefficients 190,000 coefficients**Spherical Function: BRDF**• Face Based methods are terrible (Haar-based) • Lifting doesn’t significantly help Butterfly. • Linear does better than Quadratic. 19, 73, 205 coefficients (pictures from “Spherical Wavelets”)**Up-Sampling Problems**• Smooth interpolating polynomials • over-shooting • added undulations. • Linear interpolation isn’t smooth, but results are more intuitive.**Up-Sampling Problems**• Similar problems can occur on surfaces. (picture from “Multiresolution Hierarchies On Unstructured Triangle Meshes”)**Wavelets on Spheres**• Lessons: • Prediction is hard for arbitrary data sampling • Maybe lifting isn’t necessary for very smooth subdivision schemes? • Spheres are Special: • Clearly defined DC.(??zeroth order rep, smooth rep??) • Can easily make semi-regular mesh.**Outline**• Wavelets: The Lifting Scheme • Wavelets on Triangulated Surfaces • Up-sampling problems • Applications**Triangulated Surfaces**• “It is not clear how to design updates that make the [wavelet] transform numerically stable….” (Wavelets on Irregular Point Sets) • It is difficult to design filters which after iteration yield smooth surfaces. (Wim Sweldens in personal communication)**Lifting is hard**• Prediction step is hard. • If you zero detail coefficients, you should get a “fair” surface. • Can’t use butterfly sub-division. • (picture from “Multiresolution Signal Processing For Meshes”)**Guskov et al.**• Need Smoother as part of algorithm**Guskov et al.**• Point Selection • Choose Smallest Edge • Remove one vertex in each level**Guskov et al.**• Collapse the Edge**Guskov et al.**• Prediction • Re-introduce the Edge. • Minimize Dihedral Angles • Detail Vector: Difference vector • (tangent plane coordinates)**Guskov et al.**Quasi-Update • Smooth surrounding points (minimize dihedral angles)**Guskov et al.**• Rough order of spatial frequencies. • Detail coefficients look meaningful. • Simple Smoothing: No “overshooting” errors. • No Guarantee of vanishing moments. • No Guarantee of bi-orthogonality. (picture from “Multiresolution Signal Processing For Meshes”)**Guskov et al.**• Editing (picture from “Multiresolution Signal Processing For Meshes”)**Kobbelt et al.**• Double Laplacian Smoother (thin plate energy bending minimization). • Solving PDE is slow! • Instead, solve hierarchically. (picture from “Multiresolution Hierarchies On Unstructured Triangle Meshes”)**Kobbelt et al.**• Many vertices in each step (smallest edges first) • Prediction Step: location to minimize smoothing. • Detail: Perpendicular vector to local coordinate system. • Update: Smooth surrounding points**Kobbelt et al.**• Rough order of spatial frequencies. • Fast: O(mn) with m levels, n verticies. • Many coefficients. • Bi-orthogonality? • Locality of filters? (picture from “Multiresolution Hierarchies On Unstructured Triangle Meshes”)**Are these wavelets?**• Mathematically: No. • Bi-orthogonality • Too many coefficients.**Is this representation useful?**• Patches do not wiggle; they remain in roughly the same position during down-sampling. • Smooth regions stay smooth. • Small detail coefficients. • Meaningful detail coefficients.**Outline**• Wavelets: The Lifting Scheme • Wavelets on Triangulated Surfaces • Applications • Existing • Opportunities for new research**Editing**• Replacing conventional surface editing. (NURBS) • (picture from “Multiresolution Signal Processing For Meshes” , • “Multiresolution Hierarchies On Unstructured Triangle Meshes”)**Feature Enhancement**• “For show only.” • (picture from “Multiresolution Signal Processing for Meshes”)**Compression**549 Bytes(54e-4) 1225 Bytes(20e-4) 3037 Bytes(8e-4) 18111 Bytes(1.7e-4) Original • (picture from “Normal Mesh Compression”)**Remeshing**• Go to low-resolution (to keep topology) and then sub-divide to restore original detail. • (picture from “Consistent Mesh Parameterizations”)**An Opportunity**• Analysis of the wavelet coefficients**Statistics across Meshes**• Use identical Triangulations across objects. • Look at statistics on detail coefficients rather than on points. • No global alignment problems. • No local alignment problems. • (I generated these images)**Feature Detection**• Should be able to find signature hierarchical detail coefficients. • Hard with different triangulations. • (picture from “Multiresolution Signal Processing For Meshes” )**Acknowledgements**• Professor White for suggesting the topic. • Wim Sweldens for responding to my e-mails. • Mike Halle and Steve Pieper for providing background information on the graphics community. Thank you all for coming today.**The Lifting Scheme**Mathematics Low Pass Filter: 1/8(-1,2,6,2,-1) High Pass Filter: ½(-1,2,1) Back**Solving PDEs**• Roughly, one can change the update and prediction step to have vanishing moments in the new orthogonality relationship.**Guskov et al.**• Remove vertices in smoothest regions first. • Half-Edge Collapse to remove one vertex • Add vertex in, minimizing “second order difference”. • Smooth neighbors using same minimization • Detail coefficients are the movements between initial locations and final locations.**Kobbelt et al.**• Select a fraction of the vertices. • Do half-edge collapses to remove the vertices. • Find a local parameterization around each vertex. • Add the vertex back in, minimizing the bending energy of the surface (Laplacian). • The detail vector is given by the coordinates of the point in the local coordinate system and a perpendicular height.**To Do List**• Check Sphere coefficients • Sweldons Quote: change to published quote. • Edit Guskov et al • Compression Page: comments underneath.