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Minimum Partial-Matching and Hausdorff RMS-Distance under Translation: Combinatorics and Algorithms

机译:平移下的最小局部匹配和Hausdorff RMS距离:组合和算法

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We consider the RMS-distance (sum of squared distances between pairs of points) under translation between two point sets in the plane. In the Hausdorff setup, each point is paired to its nearest neighbor in the other set. We develop algorithms for finding a local minimum in near-linear time on the line, and in nearly quadratic time in the plane. These improve substantially the worst-case behavior of the popular ICP heuristics for solving this problem. In the partial-matching setup, each point in the smaller set is matched to a distinct point in the bigger set. Although the problem is not known to be polynomial, we establish several structural properties of the underlying subdivision of the plane and derive improved bounds on its complexity. In addition, we show how to compute a local minimum of the partial-matching RMS-distance under translation, in polynomial time.
机译:我们考虑平面中两个点集之间平移时的RMS距离(点对之间的平方距离之和)。在Hausdorff设置中,每个点都与另一组中最近的邻居配对。我们开发了一种算法,用于在直线上的近线性时间以及在平面上的近二次时间中找到局部最小值。这些大大改善了流行的ICP启发式方法在解决此问题时的最坏情况。在部分匹配设置中,较小集合中的每个点都与较大集合中的不同点匹配。尽管不知道该问题是多项式的,但我们建立了该平面下层细分的几个结构特性,并针对其复杂性得出了改进的界限。此外,我们展示了如何在多项式时间内计算平移下部分匹配RMS距离的局部最小值。

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