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Automatic Detection of Tangential Discontinuities in Point Cloud Data

机译：自动检测点云数据中的切向不连续性

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摘要

A point cloud data set, a dense set of discrete coordinate points scanned or sampled from the surface of a 3D physical object or design model, is emerging as a new representation format for geometric modeling. This paper presents a new method to detect tangential discontinuities in point cloud data. The method introduces an original criterion, named as incompatibility, to quantify the magnitude of shape change in the vicinity of a data point. The introduced criterion is unique since in smooth regions of the underlying surface where shape change around a data point is small, the calculated incompatibilities tend to cluster around small values. At points close to tangential discontinuities, the calculated incompatibilities become relatively large. By modeling the incompatibilities of points in smooth regions following a statistical distribution, the proposed method identifies tangential discontinuities as those points whose incompatibilities are considered outliers with respect to the distribution. As the categorization of outliers is in effect independent of the underlying surface shape and sampling conditions of the data points, a threshold can be automatically determined via a generic procedure and used to identify tangential discontinuities. The effectiveness of the proposed method is demonstrated through many case studies using both simulated and practical point cloud data sets.
机译：点云数据集是从3D物理对象或设计模型的表面扫描或采样的密集离散坐标点集，正在成为一种用于几何建模的新表示形式。本文提出了一种检测点云数据中切向不连续性的新方法。该方法引入了称为不兼容的原始标准，以量化数据点附近形状变化的幅度。引入的标准是唯一的，因为在下表面的光滑区域中，数据点周围的形状变化很小，计算出的不兼容性趋于聚集在较小的值附近。在接近切线不连续点处，计算出的不相容性变得相对较大。通过对统计分布后的平滑区域中的点的不兼容性进行建模，所提出的方法将切向不连续性识别为那些不兼容性相对于分布而言被视为离群的点。由于离群值的分类实际上独立于基础表面形状和数据点的采样条件，因此可以通过通用过程自动确定阈值，并将其用于识别切向不连续性。通过使用仿真点云数据集和实际点云数据集的许多案例研究，证明了该方法的有效性。

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