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FastLSM: Fast Lattice Shape Matching for Robust Real-Time Deformation

FastLSM: Fast Lattice Shape Matching for Robust Real-Time Deformation. Alec R. Rivers and Doug L. James Cornell University Presenter: 이성호. Prior work: Meshless Deformations Based on Shape Matching. Best fit Rigid Transformation. Q: What can be precomputed?. Best fit Rigid Transformation.

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FastLSM: Fast Lattice Shape Matching for Robust Real-Time Deformation

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  1. FastLSM: Fast Lattice Shape Matching for Robust Real-Time Deformation Alec R. Rivers and Doug L. James Cornell University Presenter: 이성호

  2. Prior work: Meshless Deformations Based on Shape Matching

  3. Best fit Rigid Transformation Q: What can be precomputed?

  4. Best fit Rigid Transformation Q: Which is the generalized one, between R and A? Q: Prove the solution of A

  5. Extracting Rotation

  6. Particles position and velocities update

  7. Linear shape matching

  8. Linear shape matching

  9. Quadratic shape matching

  10. Best fit quadratic transformation Q: Could it be precomputed Apq and/or Aqq, and what dimensions they are?

  11. Cluster Based Deformation

  12. FastLSM

  13. Approach

  14. Assumptions • Construct regular lattice of cubic cells containing mesh • [James et al. 2004]

  15. Computational cost

  16. Naive sum

  17. Bar-plate-cube sum

  18. Constant-time sum

  19. Center of mass

  20. Rotations

  21. Goal positions Q: Prove this. (Recall in [Mueller et al. 2005], p6)

  22. Pseudocode

  23. Fast polar decomposition • Cold start (V=I) • 1.9 Jacobi sweeps/solution • 2500ns/decomposition • Warm start (V=V from the last timestep) • 0.4 Jacobi sweeps/solution • 450ns/decomposition (Refer to p5)

  24. Damping From [Mueller et al. 2006] Apply damping per-region basis (See demo)

  25. Fracture • Break by distance • [Terzopoulos and Fleischer 1988]

  26. Hardware-accelerated rendering

  27. Per-vertex normals Precompute per each vertex

  28. Constant memory restirction • Construct triangle batches

  29. Statistics

  30. Conclusion and Discussion • Lattice Shape Matching • Fast summation algorithm • Allows large deformation • Maintaining speed and simplicity • Orientation sensitive smoothing • Not physically accurate • But reasonably plausible and fast • Future works • Try different particle frameworks • Tetrahedral, irregular samplings • Adaptive particle resolution

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