Shape Registration with Spherical Cross Correlation

By Boris Gutman, Yalin Wang, Tony F. Chan, Paul M. Thompson, Arthur W. Toga. 2008

24 citations recorded by the source corpus.

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1. Taco Cohen
2. Risi Kondor
3. Mario Geiger
4. Maurice Weiler
5. David W. Romero
6. Max Welling
7. Erik J. Bekkers
8. Shubhendu Trivedi

Citations in the corpus

• Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (2018, background): “The correct way to generalize cross-correlations to the sphere is to define h(cid:63)f as a function on the rotation group itself , i.e., to set where h R is h rotated by R , expressible as with x being the point on the sphere at position ( θ, φ ) (c.f. [25][1]).”
• Spherical CNNs (2018, background): “…property of the Wigner D-functions, one can see that the Fourier coefficients can be retrieved by computing the inner product with the Wigner D-functions: Fourier convolution theorems for SO(3) and § 2 can be found in Kostelec and Rockmore (2008); Makadia et al. (2007); Gutman et al. (2008).”