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Least-squares Meshes Olga Sorkine and Daniel Cohen-Or Tel-Aviv University SMI 2004

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Our goal Shape approximation –Connectivity data –Sparse geometric data Exploit information in the mesh graph

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Connectivity Shapes Connectivity has geometric information in it [Isenburg, Gumhold and Gotsman 01] showed how to get a shape from connectivity by assuming uniform edge length and smoothness –Non-linear optimization process to get shape from connectivity Images taken from the “Connectivity Shapes”, Isenburg et al., IEEE Visualization ‘01

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Our approach Enrich the connectivity by sparse set of control points with geometry Solve a linear least-squares problem to reconstruct the geometry of all vertices (the approximated shape)

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Geometry hidden in connectivity There is geometry in connectivity

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The linear system We have two types of constraints: –Fairness constraint L(v i ) = 0 i –Geometric constraint v j = c j j C L is the mesh Laplacian operator:

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Linear least-squares solution L

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Connection to Tutte/Floater embedding Similar system was used by [Tutte 63] and [Floater 97] for graph drawing and parameterization. Constraints placed on boundary vertices along a convex 2D polygon

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Connection to Tutte/Floater embedding Tutte system has hard constraints –The smoothness condition is not satisfied at constrained vertices

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Linear least-squares solution We solve the system for 3D positions of the vertices Arbitrary topology The constraints are soft, not necessarily on a boundary L

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Solving the least-squares problem We need to solve an over-determined system: We find the solution by solving the normal equations: Very efficient solution by Cholesky factorization of A T A: –R is upper-triangular and sparse –Once R is computed, solving for x, y, z by back-substitution:

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Basis functions The geometry reconstructed by solving is in fact a combination of k basis functions:

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Basis functions The basis functions are defined on the entire mesh –Connectivity data – defined for arbitrary topology –Tagging of the control vertices The bases satisfy (in LS sense): –Smooth everywhere : Lu i = 0 –Large on the i -th control vertex ( u i = 1) and vanish on all others 5 basis functions on a 2D mesh (simple chain)

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Spectral basis vs. LS basis Another basis for compact geometry representation on meshes was proposed by [Karni and Gotsman 2000]: –The basis functions are eigenvectors of L, sorted in increasing eigenvalue order Spectral Basis The spectral basis does not take any geometric information into account Requires eigendecomposition – impractical for today’s meshes LS basis The LS basis tags specific vertices, which makes it “geometry-aware” Require solving sparse linear least-squares problem – can be done efficiently

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Selecting the control points Random selection –Fast, but less effective approximation Greedy approach –Place one-by-one at vertices with highest reconstruction error –Slow, but gives good approximation Greedy selection combined with local error maxima –A reasonable compromise Random selectionGreedy approachCombined approach 1000 control points

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Some results – varying number of control points 100 control points600 control points1200 control points3600 control points Original camel 39074 vertices

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Some results – varying number of control points 100 control points500 control points4000 control points9000 control points Original feline 49864 vertices

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Running times Pentium 2.4 GHz computer Models# verticesFactor (sec.)Solve (sec.)Total (sec.) Eight 27180.0850.0040.097 Horse 198510.9000.0320.996 Camel 390742.0960.0732.315 Feline 498642.7500.1103.080 Max Planck 1000867.7130.2408.433

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Applications Progressive geometry compression and streaming 100 control points 1000 control points 3000 control points 10000 control points 100,086 vertices, 8 seconds

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Applications Progressive geometry compression and streaming Hole filling

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Applications Progressive geometry compression and streaming Hole filling Mesh editing

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Conclusions LS-mesh represents mesh geometry using the mesh connectivity and a sparse set of control points Linear reconstruction Arbitrary topology For future work, we’d like to understand better how the mesh connectivity affects the shape or the reconstruction –governed by the shape of the basis vectors –the shape of the singular vectors of the LS system matrix A

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Acknowledgments Israel Science Foundation (founded by the Israel Academy of Sciences and Humanities) Israeli Ministry of Science German Israel Foundation (GIF) Models courtesy of Max-Planck Institut für Informatik, Stanford University, Cyberware Who created the beautiful Camel ??

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Thank you!

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High-Pass Quantization for Mesh Encoding Olga Sorkine, Daniel Cohen-Or, Sivan Toledo Eurographics Symposium on Geometry Processing, Aachen 2003.

High-Pass Quantization for Mesh Encoding Olga Sorkine, Daniel Cohen-Or, Sivan Toledo Eurographics Symposium on Geometry Processing, Aachen 2003.

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