大数据集的多视图注册

K. Pulli
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引用次数: 697

摘要

提出了一种多视点配准距离数据的方法。我们首先对扫描进行成对对齐,并使用成对对齐作为多视图步骤强制的约束,同时均匀地扩散成对配准误差。这种方法特别适合注册大型数据集,因为使用成对对齐的约束不需要将整个数据集加载到内存中来执行对齐。该对齐方法效率高,比以往的方法更不容易陷入局部最小值,并且可以与任何基于对齐重叠曲面截面的成对方法结合使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiview registration for large data sets
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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