DEVELOPMENT OF ALGORITHMS FOR POINT CLOUD FILTRATION

Richard Honti, J. Erdélyi, A. Kopáčik
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Abstract

Today, point clouds are becoming an increasingly common digital representation of realworld objects. However, the raw point clouds obtained by terrestrial laser scanning (or other methods, e. g., photogrammetry or low-cost sensors) are often noisy with many outliers. Thus, it is necessary to remove the noise and outliers from the point clouds before further processing while preserving the elements of the measured objects in high detail. Moreover, in the case of model creation from point clouds using basic geometric shapes (e.g., planes, spheres, cylinders, etc.), one of the most important processing steps is the segmentation of these shapes. Therefore, filtration of unrelated parts of the point cloud can increase the efficiency of processing. In this paper, two algorithms for point cloud filtration are developed, which can be performed based on the local point density and the local normal variation in the surrounding of the selected point. The algorithms were implemented as a standalone application in MATLAB software. The paper's final part describes the experimental testing of the proposed algorithms on several point clouds with various densities, complexity, and different levels of noise and outliers.
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点云过滤算法的发展
今天,点云正在成为现实世界中物体的一种越来越普遍的数字表示。然而,通过地面激光扫描(或其他方法,如摄影测量或低成本传感器)获得的原始点云通常具有许多异常值的噪声。因此,在进一步处理之前,有必要去除点云中的噪声和异常值,同时保持被测物体的高细节元素。此外,在使用基本几何形状(例如,平面,球体,圆柱体等)从点云创建模型的情况下,最重要的处理步骤之一是这些形状的分割。因此,对点云中不相关的部分进行过滤,可以提高处理效率。本文提出了两种点云过滤算法,分别基于点的局部密度和点周围的局部正态变化进行点云过滤。这些算法在MATLAB软件中作为独立应用程序实现。论文的最后一部分描述了在具有不同密度、复杂性和不同噪声和异常值水平的几个点云上对所提出算法的实验测试。
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