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Least-squares fitting of multiple M-dimensional point sets

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Abstract

Based on the classic absolute orientation technique, a new method for least-squares fitting of multiple point sets in m-dimensional space is proposed, analyzed and extended to a weighted form in this paper. This method generates a fixed point set from k corresponding original m-dimensional point sets and minimizes the mean squared error between the fixed point set and these k point sets under the similarity transformation. Experiments and interesting applications are presented to show its efficiency and accuracy.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China

    Gaojin Wen, Zhaoqi Wang, Shihong Xia & Dengming Zhu

  2. Graduate school of the Chinese Academy of Sciences, 100080, Beijing, China

    Gaojin Wen & Dengming Zhu

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  1. Gaojin Wen
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  2. Zhaoqi Wang
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  3. Shihong Xia
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  4. Dengming Zhu
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Correspondence to Gaojin Wen.

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Wen, G., Wang, Z., Xia, S. et al. Least-squares fitting of multiple M-dimensional point sets. Visual Comput 22, 387–398 (2006). https://doi.org/10.1007/s00371-006-0022-6

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  • DOI: https://doi.org/10.1007/s00371-006-0022-6

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