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

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Presentation transcript:

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 Q: Which is the generalized one, between R and A? Q: Prove the solution of A

Extracting Rotation

Particles position and velocities update

Linear shape matching

Linear shape matching

Quadratic shape matching

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

Cluster Based Deformation

FastLSM

Approach

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

Computational cost

Naive sum

Bar-plate-cube sum

Constant-time sum

Center of mass

Rotations

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

Pseudocode

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)

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

Fracture Break by distance [Terzopoulos and Fleischer 1988]

Hardware-accelerated rendering

Per-vertex normals Precompute per each vertex

Constant memory restirction Construct triangle batches

Statistics

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