Iterator of set


  • Reference
  • Deformation energy
    • Geometric energy to strtch and band thin shell from one shape t another as difference between first and second fundamental form
      • First: stretching
      • second: bending
    • Approach:
      • Given constraints (handle position / orientation, find surface that min deformation energy)
    • Linear Approximation
      • Energy based on fundamental forms in non-linear function of displacements
        • Hard to minimize
      • linear approximation using partial derivatives of displacement function.
        • Assume parameterization of displacement field d(u,v)
        • Bending:
          • linear energy:
            • laplace = 0 minimize surface area
          • Variational calculus, Euler0-Lagrange equations:
            • laplace of laplace: make it smooth (the derivative of surface change continuously and is minimized)
          • So, apply bi-laplacian on mesh
    • Skeletal animation

Nov14. Mesh Smoothing

  • Mesh smoothing:
    • local averaging
    • minimize local gradient energy in 3 dimensions
    • Fourier transform (low pass filter) similar to local averaging idea
      • image convolution
      • F(A*B) = F(A) * F(B)
  • Spectral Analysis
    • In general: extending eigenvalues, eigenvectors to linear operators on (continuous) functions.
    • Fourior transform:
      • approximate signal as weighted sum (linear combination) of sines and cosines of different frequencies.
      • change of basis using eigenfunctions of Laplace operator (complex exponentials including sines and cosines)
      • Fourier transform function: {\displaystyle {\hat {f}}(\xi )=\int _{-\infty }^{\infty }f(x)\ e^{-2\pi ix\xi }\,dx,}
      • spacial domain–>frequency domain F(epsilon) complex amplitude
      • Inverse transform:{\displaystyle f(x)=\int _{-\infty }^{\infty }{\hat {f}}(\xi )\ e^{2\pi ix\xi }\,d\xi ,}
      • denoising: fourier transform–>filter out high frequency–>fourior inverse transform
    • For mesh:
      • Intuition: Fourior transform by projecting onto eigenfunctions of Laplacian
      • mesh laplacian L is n x n matrix,  n is number of vertices
        • Use PSD L (not normalized by vertex valence of voronoi area)
        • eigenvectors orthogonal
      • Project geometry onto eigenvectors.
      • reconstruction from eigenvectors associated with low frequencies
      • Chanllenge:
        • Too complex!
        • Too much computation!
  • Diffusion
    • Laplace smoothing
      • Laplace is second derivative.
      • Smooth with Gaussian kernel.
      • backward Euler
        • solve p’ = p + mu * dt *L * p’
        • (I – mu * dt * L) p’ = p, identity matrix I
        • solve linear system for  p’ in each step
        • Advantages: Allow larger time steps, no numerical stability problems.
  • Energy minimization
  • Alternatives

Falcor Material and Light Load


740 Oct26

How to solve Linear list square min||Ax – b||^2??

How to find the transformation matrix if we don’t have the correspondence between two point clouds??

Build ruined 3D mesh:

  1. Voronoi diagram
  2. Delaunay triangulation
  3. Crust in 3D

Post processing:

  • normal estimation and filtering

Build the curve when we are given the surface points and the normals??

  • triangulation Iso-surface??

Commonly Used Variables in Falcor

136. Single Number

Given an array of integers, every element appears twice except for one. Find that single one.