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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method
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An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method

机译：基于多尺度张量投票方法的TLS点云平面分割的精确鲁棒区域增长算法

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摘要

The accuracy and robustness of plane segmentation using a region-growing algorithm remains an important and challenging topic for terrestrial laser scanning point clouds. The plane segmentation of a region-growing algorithm depends heavily on the seed point, as there are currently no universally valid criteria. This article proposes a multiscale tensor voting method (MSTVM) to determine the appropriate seed point for the region-growing algorithm. A comprehensive plane strength indicator calculated by the semivariogram model has been established to assess whether a certain point is suitably considered as a seed point or not. A point cloud containing 17,881 points in a 400-m(2) area was selected to validate the proposed algorithm. The results suggest that the scale range calculated by the semivariogram model can effectively mitigate the scale effect of the tensor voting method (TVM). The comprehensive plane strength of our proposed algorithm in seed point determination is shown to be more salient than the principal component analysis and the TVM. The findings further reveal that the utility of the MSTVM-based region-growing algorithm can achieve more accurate plane segmentation results and perform with better robustness in noisy point clouds. This allows our proposed method to be more widely applied to complex real situations.
机译：对于地面激光扫描点云而言，使用区域增长算法进行平面分割的准确性和鲁棒性仍然是一个重要且具有挑战性的主题。区域增长算法的平面分割在很大程度上取决于种子点，因为目前尚无普遍有效的标准。本文提出了一种多尺度张量投票方法（MSTVM），可以为区域增长算法确定合适的种子点。已经建立了由半变异函数模型计算的综合平面强度指标，以评估某个点是否适当地视为种子点。选择一个在400 m（2）区域中包含17,881个点的点云来验证所提出的算法。结果表明，半变异函数模型计算出的尺度范围可以有效缓解张量投票方法（TVM）的尺度效应。结果表明，我们提出的算法在种子点确定中的综合平面强度比主成分分析和TVM更突出。这些发现进一步揭示了基于MSTVM的区域增长算法的实用性可以实现更精确的平面分割结果，并且在嘈杂的点云中具有更好的鲁棒性。这使我们提出的方法可以更广泛地应用于复杂的实际情况。

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