Analysis of weighting of normals for spherical harmonic cross-correlation

Abstract

Spherical harmonic cross-correlation is a robust registration technique that uses the normals of two overlapping meshes to bring them into coarse rotational alignment. The amount of overlap between the two meshes is the primary determinant of whether the spherical harmonic cross-correlation achieves correct registration. By weighting each normal or clusters of normals, their contribution to the registration is influenced, allowing beneficial normals to be emphasized and deemphasizing those that are not. In this paper we evaluate how different weighting schemes impact registration efficiency and accuracy. It is found that two of the proposed weighting schemes are capable of correctly registering 22% of the mesh pairs, while the baseline, which equally weighted all normals, registered 14% of the mesh pairs. Using Fibonacci binning to equally weight surfaces provided the best all-round advantage, especially if efficiency is considered, as binning allows spherical harmonics to be pre-computed. By increasing the threshold that is applied to the weighting schemes, meshes with minimal overlap can be registered, with one case only having 2% overlap. The performed analysis shows that weighting normals when applied in a conducive manner can achieve considerable improvements improvements to registration accuracy.

DOI: 10.1117/12.2008434

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Cite this paper

@inproceedings{Larkins2013AnalysisOW, title={Analysis of weighting of normals for spherical harmonic cross-correlation}, author={Robert L. Larkins and Michael J. Cree and Adrian A. Dorrington}, booktitle={Three-Dimensional Image Processing and Applications}, year={2013} }