1. CSE 554 Lecture 9: Laplacian Deformation
Fall 2013

2. Review Alignment Methods
Source
Alignment
Registering source to target by rotation and translation
Rigid-body transformations
Methods
Aligning principle directions (PCA)
Aligning corresponding points (SVD)
Iterative improvement (ICP)
Combines PCA and SVD
Input
Target
After PCA
After ICP

3. Non-rigid Registration
Rigid alignment cannot account for shape variance
Non-rigid deformation can give a better fit
Source
Target
Rigid alignment
After non-rigid deformation

4. Non-rigid Registration
A minimization problem
Minimizing the distance between the deformed source and the target
“Fitting term”
Minimizing the distortion to the source shape
“Distortion term”

5. Intrinsic vs. Extrinsic
Intrinsic methods
Deforms points on the source curve/surface
App: boundary curve or surface matching
Extrinsic methods
Deforms all points on and interior to the source curve/surface
App: image or volume matching

6. Laplacian-based Deformation
An intrinsic method
Simple, efficient, producing reasonable results
Preserving local shape features
Widely used in graphics applications for interactive deformation
Reference: “Laplacian surface editing”, by Sorkine et al., 2004 (citation ~ 500)

7. Setup Input Output Source with n points: p1,…,pn
Let the first m points be “handles”
Target location of handles: q1,…,qm
Output
Deformed locations of source points: p1’,…,pn’
When deforming the source to fit a target shape: m=n and qi is the point on the target closest to pi.
Deformed
Source q2
p3=q3
p1=q1 p2
An example with 3 target points, two of which are stationary (red)

8. Overview Finding deformed locations pi’ that minimize:
Ef: fitting term
Measures how close are the deformed handles to the target
Ed: distortion term
Measures how much the source shape is changed

9. Fitting Term
Sum of squared distances to target handle locations q2 p2

10. Distortion Term Q: How to measure shape?
A: By “bumpiness” at each vertex
Laplacian: vector from the centroid of neighbors to the vertex
Recall that in fairing, we reduced this vector to “smooth out” bumps
A linear operator over point locations
where Ni are indices of neighboring vertices of pi

11. Distortion Term Minimizing changes in Laplacians during deformation
Over all source points
i: Laplacian at pi before deformation

12. Putting Together Finding deformed locations pi’ that minimize:
A quadratic equation in terms of variables (pix’, piy’, piz’)
qi, i are constants
L[] is a linear operator

13. Quadratic Minimization
A general form of quadratic minimization:
There are s variables: x=(x1,…,xs)T
Each a1,…, ak is a length-s column vector (linear coefficients)
Each b1,…, bk is a scalar (constant coefficients)
k should be greater than s (so that the problem is over-constrained)

14. Quadratic Minimization
To solve:
Re-write in matrix form:
where
is a k by s matrix
is a length-k vector

15. Quadratic Minimization
The minimizer is where the partial derivatives are all zero
To solve for x in this equation:
Taking matrix inverse (good for small s, but numerically unstable for large s)
Using specialized linear system solver (LinearSolve in Mathematica, TNT/LAPACK in C)

16. Quadratic Minimization
Re-writing our minimization in the general form
In 2D, there are s=2n variables: x = (p1x’,…, pnx’, p1y’,…, pny’ )T
In 3D, there are s=3n variables
We will next re-write each quadratic term in 2D as (aix-bi)2
Can be extended easily to 3D

17. Quadratic Minimization
The ai and bi in the fitting term
There are 2m quadratic terms
In the first set of m terms:
For i=1,…,m, bi=qix, ai contains all zero, except its (i)th entry is 1.
In the second set of m terms:
For i=1,…,m, bi+m=qiy, ai+m contains all zero, except its (i+n)th entry is 1

18. Quadratic Minimization
The ai and bi in the fitting term
There are 2m quadratic terms
Example with 3 vertices and 2 fitting constraints (n=3; m=2):

19. Quadratic Minimization
The ai and bi in the distortion term:
There are 2n quadratic terms
The first set of n terms:
For i=1,…,n, ai is all zero except the (i)th entry is 1, the (j)th entries are -1/|Ni| for all jNi, and bi=ix
The second set of n terms:
For i=1,…,n, ai+n is all zero except the (i+n)th entry is 1, the (j+n)th entries are -1/|Ni| for all jNi, and bi+n=iy

20. Quadratic Minimization
The ai and bi in the distortion term:
There are 2n quadratic terms
Example with 3 vertices (n=3):

21. Summary Compute Laplacians (i) Construct coefficients (ai, bi)
Put them into matrices (A,B)
Solve (x)

22. Results
Deformed
A small deformation

23. Results
Deformed
A larger deformation

24. Results
Deformed
Stretching

25. Results
Deformed
Shrinking

26. Results
Deformed
Rotation

27. Discussion Limitations
Local features are “skewed”, and they don’t scale with the model
Reason: Laplacian changes with rotation or scale
Two bumps that differ by rotation or scale have different Laplacians
Which will be penalized by our distortion term

28. A Better Distortion Term
Not penalizing rotation and scaling of local features
Transforming the original Laplacian vectors before comparing to the deformed Laplacians
Ti is a matrix that describes how the local shape around pi is deformed

29. Key Questions How to represent transformations as matrices?
How to compute Ti?
We will focus in the derivations of the 2D case
3D results will be briefly presented at the end

30. Transformation Matrices (2D)
Homogeneous coordinates
A 2D point: (x,y,1)
A 2D vector: (x,y,0)
A 3D point: (x,y,z,1)
A 3D vector: (x,y,z,0)

31. Transformation Matrices (2D)
Translation
Cartesian coordinates: vector addition
Homogeneous coordinates: matrix product

32. Transformation Matrices (2D)
Isotropic scaling
Cartesian coordinates: vector scaling
Homogeneous coordinates: matrix product

33. Transformation Matrices (2D)
Rotation
Cartesian coordinates: matrix product
Homogeneous coordinates: matrix product

34. Transformation Matrices (2D)
Summary of elementary similarity transformations
To combine transformations: take the product of these matrices
Translation by vector v
Scaling by scalar s
Rotation by angle 

35. Similarity Transforms (2D)
General similarity transformations
The product of any set of elementary matrices can be written this way
Any choice of (a, w, tx, ty) can be written as a sequence of rotation, isotropic scaling and translation
Note that a and w can’t be both zero

36. Computing Ti (2D) Suppose we know the deformed locations pi’
Compute Ti as the similarity transform that best fits the neighborhood of pi to that of pi’

37. Computing Ti (2D) Suppose we know the deformed locations pi’
Compute Ti as the similarity transform that best fits the neighborhood of pi to that of pi’
This is a quadratic minimization problem for entries of Ti
E.g., a, w, tx, ty

38. Computing Ti (2D) The matrix form of the minimization is: where
is a 2|Ni|+2 by 4 matrix, and Ni={i1, i2,…} are indices of neighboring vertices of pi

39. Computing Ti (2D) By quadratic minimization:
Linear expressions of variables (pix’ , piy’)

40. Distortion Term (2D) Two parts of each distortion term:
Transformed Laplacian:
Laplacian of the deformed locations:
where
where
is a 2 by 2|Ni|+2 matrix

41. Distortion Term (2D) Putting together:
They form 2n quadratic terms (aix-bi)2 for x = (p1x’,…, pnx’, p1y’,…, pny’ )T
All bi are zero
Each ai can be extracted from H
where
and
are its rows

42. Results (2D)
Old distortion term
New distortion term

43. Results (2D)
Old distortion term
New distortion term

44. Results (2D)
Old distortion term
New distortion term

45. Results (2D)
Old distortion term
New distortion term

46. Registration Use nearest neighbors as corresponding target locations
Assuming the source is already close to the target
Iterative closest point (ICP)
1. For each point on the source pi, treat it as a handle, and assign its closest point on the target as its target location qi. Compute Laplacian- based deformation.
A threshold on the closest distance can be used to throw away unlikely correspondences
2. Repeat step (1) until a termination criteria is met.
Maximum iteration or minimum RMSD improvement

47. Result After rigid alignment 1 iteration of Laplacian
7 iterations of Laplacian
Overlaying all curves

48. Result
Weighting the distortion term
large w
medium w
small w

49. Similarity Transforms (3D)
Elementary transformation matrices
To perform a sequence of transformations: take the product of these matrices
Translation by vector v
Scaling by scalar s
Rotation by angle  around X axis

50. Similarity Transforms (3D)
General similarity transformations in 3D
Approximates the product of a set of elementary matrices
Up to a small rotation angle
May introduce skewing for large rotations

51. Computing Ti (3D)
Assuming known deformation, by quadratic minimization:
Linear expressions of the deformed points pi’
C is a 3|Ni|+3 by 7 matrix
where

52. Distortion Term (3D)
Constructing transformed Laplacian:
where
where