【点云论文速读】Scale invariant line-based co-registration

标题：Scale invariant line-based co-registration of multimodal aerial data using L1 minimization of spatial and angular deviations

作者：Przemyslaw Polewski, Wei Yao

来源：.ISPRS Journal

星球ID：Lionheart|点云配准

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●论文摘要

使用2D和3D线特征来计算不同类型模型的配准结果。这项工作中，我们研究使用对应线特征完成多模型数据配准，例如摄影测量点云/LiDAR点云数据，数字表面模型数据，正射影像数据，或者3Dcad城市模型，3D线从两个相邻平面交线得到，面交线相比于单点特征提取方式更为鲁棒，我们提出了两步策略来匹配模型，首先从两个数据集合中使用尺度不变图匹配思想寻找最优的线匹配，然后使用匹配结果计算最优的转换参数，我们的方法可以使用多个线对来计算最优的转换参数，与其他的方法相对比，我们转化计算是基于对应线对最小化平均L1距离来，假设模型考虑计算尺度因子，三个转换参数和三个转换角度，我们的实验使用两个ISPRS公开数据集，同时使用三种方法与所提方法进行对比，结果表明，在Z轴预先固定的情况下，L1距离的方法降低了线对间的三分之一的距离，更多的，当共同匹配两个从差异性很大的摄影测量点云时，我们的方法能够使得匹配的行数增加两倍，我们的结果表明，与通常的基于一致性的方法相比，基于更多的线对来进行转换计算是值得的。我们建立的基于线的配准验证数据集已经发布并在网上可用：(https://doi.org/10.17632/dmp7tkn8kc.2).

●论文图集

基于聚类的面提取与面交线的提取

线对上采点计算评价线对间距离

不同类型数据的配准结果

●英文摘要

In this work, we investigate the coregistration of multimodal data, such as photogrammetric/LiDAR point clouds, digital surface models, orthoimages, or 3D CAD city models, using corresponding line segments. The lines are analytically derived as intersections of adjacent planar surfaces, which can be determined more robustly and are deemed more accurate compared to single point based features. We propose a two-stage approach, which frst focuses on fnding optimal line correspondences between the datasets using a scale-invariant graph matching method, and then utilizes the found matching as a basis for calculating the optimal coregistration transform. By decoupling the correspondence search from the transform calculation, our approach can use more line pairs for determining the optimal transform than would be practicable with a combined, sampling-style approach. As opposed to competing methods, our transform computation is based on explicitly minimizing the average L1 distance on the matched line set. The assumed model accounts for an isotropic scaling factor, three translations and three rotation angles. We conducted experiments on two publicly available ISPRS datasets: Vaihingen and Dortmund, and compared the performance of several variations of our approach with three competing methods. The results indicate that the L1 methods decreased the median matched line distance by up to one third in case of pre-aligned Z axes. Moreover, when coregistering two photogrammetric datasets acquired from distinct viewing perspectives, our method was able to triple the number of matched lines (under a strict proximity-based criterion) compared to its competitor. Our results show that it is worthwhile to base the transform calculation on signifcantly more line pairs than is customary for sample consensus-based approaches. Our established validation dataset for line-based coregistration has been published and made available online