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Kernel functions for robust 3D surface registration with spectral embeddings

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4 Author(s)
Xiuwen Liu ; Dept. of Comput. Sci., Florida State Univ., Tallahassee, FL ; Donate, A. ; Jemison, M. ; Mio, W.

Registration of 3D surfaces is a critical step for shape analysis. Recent studies show that spectral representations based on intrinsic pairwise geodesic distances between points on surfaces are effective for registration and alignment due to their invariance under rigid transformations and articulations. Kernel functions are often applied to the pairwise geodesic distances to make the registration process based on spectral embedding robust to elastic deformations. The Gaussian kernel is most commonly used, but the effect of the choice of the kernel function has not been studied in the previous works. In this paper, we compare the results obtained with several different choices and show empirically that significant improvements can be achieved in shape registration with appropriate choices.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008