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Multiview registration for large data sets

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1 Author(s)
K. Pulli ; Stanford Univ., CA, USA

We present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections

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3-D Digital Imaging and Modeling, 1999. Proceedings. Second International Conference on

Date of Conference: