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Mapping & Warping shapes Geometry Acquisition

Mapping & Warping shapes Geometry Acquisition. Zheng Hanlin 2011.07.05. -- Summer Seminar. Papers. Bounded Biharmonic Weight for Real-Time Deformation (SIG11) Biharmonic Distance (TOG11) Blended Intrinsic Maps (SIG11) Photo-Inspired Model-Driven 3D Object Modeling (SIG11)

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Mapping & Warping shapes Geometry Acquisition

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  1. Mapping & Warping shapesGeometry Acquisition Zheng Hanlin 2011.07.05 -- Summer Seminar

  2. Papers • Bounded Biharmonic Weight for Real-Time Deformation (SIG11) • Biharmonic Distance (TOG11) • Blended Intrinsic Maps (SIG11) • Photo-Inspired Model-Driven 3D Object Modeling (SIG11) • Style-Content Separation by Anisotropic Part Scales (SIGA10) • L1-Sparse Reconstruction of Sharp Point Set Surfaces (TOG) • GlobFit: Consistently Fitting Primitives by Discovering Global Relations (SIG11) • Data-Driven Suggestions for Creativity Support in 3D Modeling (SIGA10)

  3. Bounded Biharmonic Weight for Real-Time Deformation Sig11

  4. Authors • Alec Jacobson • Ph.D. Candidate

  5. Authors • Ilya Baran • Postdoc. • Disney Research in Zurich

  6. Authors Olga Sorkine Assistant Professor ETH Zurich

  7. The Main Idea • Shape deformation • Work freely with the most convenient combination of handle types bone points cage

  8. Motivation(Video) • Typical flow for deformation • Bind the object to handles (bind time) • Manipulate the handles (pose time) • Different handle types have different advantages and disadvantages • Design the weights for a linear blending scheme • Real-time responce

  9. Motivations

  10. Algorithm • Linear blending: Handle size Old position New position Affine transformation of handle Hj Weight function Bounded biharmonic weights

  11. Algorithm • Bounded biharmonic weights:

  12. Algorithm • Bounded biharmonic weights: • Properties: • Smoothness • Non-negativity • Shape-awareness • Partition of unity • Locality and sparsity • No local maxima

  13. Algorithm • Bounded v.s. Unbounded

  14. Results & Comparison

  15. Results

  16. Results

  17. Results

  18. Results

  19. Performance

  20. Limitation • The optimization is not fast enough • Bind-time • This weights do NOT have the linear precision property

  21. Conclusion • Unify all popular types of control armatures • Intuitive design of real-time blending deformation

  22. Biharmonic Distance TOG11

  23. Authors Thomas Funkhouser Yaron Lipman Raif M. Rustamov

  24. The Main Idea • A new distance measure based on the biharmonic differential operator

  25. Motivation • The most important properties for a distance • metric • smooth • Locally isotropic • Globally shape-aware • Isometry invariant • Insensitive to noise • Small topology changes • Parameter free • Practical to compute on a discrete mesh • … Does there exist a measure cover all these properties?

  26. Related works • Geodesic distance • Not smooth, insensitive to topology • Diffusion distance • Not locally isotropic • Not global shape-aware • Depending on parameter • Commute-time distance (Graph) • Cannot define on surfaces • Depending on the conformal structure

  27. Algorithm • Continuous cases: • Biharmonic: Green’s function

  28. Algorithm • Discrete cases • Can be proved: Conformal discrete laplacian

  29. Results & Comparisons

  30. Results & Comparisons

  31. Applications • Function interpolation on surfaces

  32. Applications • Surface matching

  33. Performances

  34. Conclusions • A novel surface distance • Has good properties

  35. Blended Intrinsic Maps Sig11

  36. Authors • Vladimir G. Kim • Ph.D. Candidate • Princeton Univ. • He has Canadian and Kyrgyz citizenships.

  37. Authors • Thomas Funkhouser • Yaron Lipman

  38. The Main Idea • Find the maps between two genus 0 surfaces

  39. Related Works • Inter-surface mapping • Finding sparse correspondences • Iterative closest points • Finding dense correspondences • Surface embedding • Exploring Mobius Transformations

  40. Algorithm • Blended map Candidate maps Smooth blending weights

  41. Algorithm • Generating maps (candidate conformal maps) • Defining confidence weights • How much distorting is induced • Finding consistency weights • Lower values for incorrect matches • Blend map

  42. More Details • Finding Consistency Weights • Objective Function • Similarity measure • Optimizing

  43. Results & Comparisons

  44. Results & Comparisons

  45. Results & Performances

  46. Results & Performances

  47. Limitation & Conclusion • Limitations: • Not guaranteed to work in case of partial near-isometric matching • Only for genus zero surfaces now • An automatic method for finding a map between surfaces (including non-isometric surfaces)

  48. Photo-Inspired Model-Driven 3D Object Modeling Sig11

  49. The Main Idea • Modeling • From single photo

  50. Workflow

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