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Alignment

Alignment. Introduction. Notes courtesy of Funk et al. , SIGGRAPH 2004. Outline: Challenge General Approaches Specific Examples. Translation. Rotation. Scale. Alignment. Challenge : The shape of a model does not change when acted on by similarity transformations:. =. Alignment.

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Alignment

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  1. Alignment Introduction Notes courtesy of Funk et al., SIGGRAPH 2004

  2. Outline: • Challenge • General Approaches • Specific Examples

  3. Translation Rotation Scale Alignment Challenge: The shape of a model does not change when acted on by similarity transformations: =

  4. Alignment Challenge: However, the shape descriptors can change if a the model is: • Translated • Scaled • Rotated How do we match shape descriptors across transformations that do not change the shape of a model?

  5. Outline: • Challenge • General Approaches • Specific Examples

  6. Alignment Approaches: Given the shape descriptors of two models, find the transformation(s) -- translation, scale, and rotation -- that minimize the distance between the two models: • Exhaustive search • Closed form solution • Minimization • Normalization • Invariance

  7. Alignment Aside: Because translations and rotations preserve distances, applying such a transformation to one model is equivalent to applying the inverse transformation to the other one: =

  8. Alignment Aside: For translations and rotations we can simplify the alignment equation:

  9. Exhaustive Search Approach: • Compare the descriptors at all possible transformations. • Find the transformation at which the distance is minimal. • Define the model similarity as the value at the minimum.

  10. Exhaustive Search Approach: • Compare the descriptors at all possible transformations. • Find the transformation at which the distance is minimal. • Define the model similarity as the value at the minimum. Exhaustive search for optimal rotation

  11. Exhaustive Search Approach: • Compare the descriptors at all possible transformations. • Find the transformation at which the distance is minimal. • Define the model similarity as the value at the minimum.

  12. Exhaustive Search Properties: • Always gives the correct answer • Needs to be performed at run-time and can be very slow to compute: Computes the measure of similarity for every transform. We only need the value at the best one.

  13. Closed Form Solution Approach: Explicitly find the transformation(s) that solves the equation: Properties: • Always gives the correct answer • Only compute the measure of similarity for the best transformation. • A closed form solution does not always exist. • Often needs to be computed at run-time.

  14. Minimization Approach: • Coarsely align the models using low frequency information. • Progressively refine the alignment by comparing higher frequency components and adjusting the alignment. • Converge to the (locally) optimal alignment. Example: Light field descriptors Spherical Extent Function

  15. Minimization Approach: • Coarsely align the models using low frequency information. • Progressively refine the alignment by comparing higher frequency components and adjusting the alignment. • Converge to the (locally) optimal alignment. Initial Models Low Frequency Aligned Models

  16. Minimization Approach: • Coarsely align the models using low frequency information. • Progressively refine the alignment by comparing higher frequency components and adjusting the alignment. • Converge to the (locally) optimal alignment. = + = + Initial Models Low Frequency High Frequency Aligned Models

  17. Minimization Properties: • Can be applied to any type of transformation • Needs to be computed at run-time. • Difficult to do robustly: Given the low frequency alignment and the computed high-frequency alignment, how do you combine the two? Considerations can include: • Relative size of high and low frequency info • Distribution of info across the low frequencies • Speed of oscillation

  18. Translation Rotation Scale Normalization Approach: Place every model into a canonical coordinate frame and assume that two models are optimally aligned when each is in its own canonical frame. Example: COM, Max Radius, PCA

  19. Normalization Properties: • Can be computed in a pre-processing stage. • For some transformations this is guaranteed to give the optimal alignment. • For other transformations the approach is only a heuristic and may fail. Failure of PCA-normalization in aligning rotations

  20. Translation Rotation Scale Invariance Approach: Represent every model by a descriptor that is unchanged when the model is transformed by discarding information that is transformation dependent. Transformation-invariant descriptor

  21. Invariance Review: Is there a general method for addressing these basic types of transformations?

  22. Invariance Properties: • Can be computed in a pre-processing stage. • Works for translation, scale and rotation. • Gives a more compact representation. • Tends to discard valuable, discriminating, information. …….....No Invariance ……..……Rotation …Translation + Rotation ………….…..Rotation

  23. Outline: • Challenge • General Approaches • Specific Examples • Normalization: PCA • Closed Form Solution: Ordered Point Sets

  24. PCA Alignment Treat a surface as a collection of points and define the variance function:

  25. PCA Alignment Define the covariance matrix M: Find the eigen-values and align so that the eigen-values map to the x-, y-, and z-axes

  26. PCA Alignment Limitations: • Eigen-values are only defined up to sign!PCA alignment is only well-defined up to axial flips about the x-, y-, and z-axes.

  27. PCA Alignment Limitations: • Assumes that the eigen-values are distinct and therefore the eigen-vectors are well-defined (up to sign). This is not always true and can make PCA alignment unstable.

  28. Outline: • Challenge • General Approaches • Specific Examples • Normalization: PCA • Closed Form Solution: Ordered Point Sets

  29. Ordered Point Sets Challenge: Given ordered point sets P={p1,…,pn}, Q={q1,…,qn}, find the rotation/reflection R minimizing the sum of squared differences: q4 q4 p2 R(q2) p3 p1 R(q1) R(q3) R q5 q5 q3 q3 q6 q6 q2 q2 p4 p6 R(q6) R(q4) p5 R(q5) q1 q1

  30. Review Vector dot-product: If v =(v1,…,vn) and w=(w1,…,wn) are two n-dimensional vectors the dot-product of v with w is the sum of the product of the coefficients:

  31. Review Trace: The trace of a nxn matrix M is the sum of the diagonal entries of M: Properties:

  32. Review Trace: If M is any nxn matrix and D is a diagonal nxn matrix, then the trace of MD is the sum of the products of the diagonal entries. M D

  33. Review Matrix multiplication: If M and N are two then the (i,j)th entry of the matrix MN is the dot-product of the jth row vector of M with the ith column vector of N. M MN N jth row ith column (i,j)th entry

  34. Review Matrix dot-product: If M and N are two mxn matrices then the ith diagonal entry of the matrix MtN is the dot-product of the ith column vector of M with the ith column vector of N. m n Mt MtN N n m ith row ith column ith diagonal entry

  35. Review Matrix dot-product: We define the dot-product of two mxn matrices, M and N, to be the trace of the matrix product: (the sum of the dot-products of the column vectors).

  36. Review SVD Factorization: If M is an mxm matrix, then M can be factored as the product: where D is a diagonal mxm matrix with non-negative entries and U and V are orthonormal (i.e. rotation/reflection) mxm matrices.

  37. Trace Maximization Claim: If M is an mxm matrices, whose SVD factorization is: then the orthonormal transformation R=VUt is the orthonormal transformation maximizing the trace:

  38. Trace Maximization Proof: We can rewrite the trace equation as: If we set R0 to be the rotation R0=VtRU, we get:

  39. Trace Maximization Proof: Since R0 is a rotation, each of its entries can have value no larger than one. Since D is diagonal, the value of the trace is the product of the diagonal elements of D and R0.

  40. Trace Maximization Proof: To maximize the trace we want R0 to be maximal on the diagonal (i.e. have only 1’s). Thus, R0 is the identity matrix and we have: So that the rotation/reflection R that maximizes Trace(RM) is:

  41. Ordered Point Sets Challenge: Given ordered point sets P={p1,…,pn}, Q={q1,…,qn}, find the rotation/reflection R minimizing the sum of squared differences:

  42. Ordered Point Sets Solution: Use the fact that we can express the difference: to rewrite the equation as:

  43. Ordered Point Sets Solution: Use the fact that rotations preserves lengths: to rewrite the equation as:

  44. Ordered Point Sets Solution: Since the value: Does not depend on the choice of rotation R, minimizing the sum of squared distances is equivalent to maximizing the sum of dot-products:

  45. Ordered Point Sets Solution: If we let MP (respectively MQ) be the 3xn matrix whose columns are the points pi (respectively qi) then we can rewrite the sum of the vector dot-products as the matrix dot product: and we can find the maximizing rotation/reflection R by trace maximization.

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