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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Penalizing Closest Point Sharing for Automatic Free Form Shape Registration
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Penalizing Closest Point Sharing for Automatic Free Form Shape Registration

机译：罚分最近点共享以自动注册自由形状形状

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摘要

For accurate registration of overlapping free form shapes, different points in one shape must select different points in another as their most sensible correspondents. To reach this ideal state, in this paper we develop a novel algorithm to penalize those points in one shape that select the same closest point in another as their tentative correspondents. The novel algorithm then models the relative weight change over time of a tentative correspondence as the difference between the negative functions of the numbers of points in one shape that actually and ideally select the same closest point in another. Such modeling results in an optimal estimation of the weights of different tentative correspondences, in the sense of deterministic annealing, that lead the camera motion parameters to be estimated in the weighted least squares sense. The proposed algorithm is initialized using the pure translational motion derived from the centroids difference of the overlapping free form shapes being registered. Experimental results show that it outperforms three selected state-of-the-art algorithms on the whole for the accurate and robust registration of real overlapping free form shapes captured using two different laser scanners under typical imaging conditions.
机译：为了准确地记录重叠的自由形式形状，一种形状中的不同点必须选择另一种形状中的不同点作为它们最敏感的对应物。为了达到这种理想状态，在本文中，我们开发了一种新颖的算法来惩罚那些选择一种形状的点，将另一种形状的最近点选作它们的暂定对应点。然后，该新颖算法将暂时对应关系的相对权重变化建模为一种形状中实际上理想地选择另一种形状中的同一最接近点的点数的负函数之差。在确定性退火的意义上，这种建模导致对不同的暂定对应关系的权重的最佳估计，这导致在加权最小二乘意义上估计摄像机运动参数。所提出的算法是使用纯平移运动初始化的，该平移运动是根据重叠的自由形式形状的形心差得出的。实验结果表明，对于在典型成像条件下使用两个不同的激光扫描仪捕获的真实重叠自由形式形状的准确而可靠的配准，它总体上优于三个选定的最新算法。

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