基于多尺度几何测度的点云邻域可视化分析

Marcel Ritter, D. Schiffner, M. Harders
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引用次数: 1

摘要

点集是一种广泛应用于计算和观测领域的空间数据结构,如物理粒子模拟、计算机图形学或遥感。算法通常在点集的局部邻域中运行,用于计算物理状态、表面重建等。提出了一种基于点云多尺度几何特征的可视化技术。我们在不同的点集几何形状和不同的噪声水平上探讨了不同选择的基础加权协方差邻域描述符的性质。不同的权重函数和张量质心以及点集特征和噪声水平的影响在旋转不变的特征图像中变得可见。我们比较了基于曲率的尺度空间可视化方法,最后展示了如何通过在交互式工具中使用我们的方法创建的图像来检查真实世界LiDAR数据中的特征。与基于曲率的方法相比,我们的方法在不断增长的尺度上突出显示线结构,具有平面或球形几何结构的清晰边界区域。CCS概念•以人为中心的计算→可视化分析;•计算方法→基于点的模型;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Analysis of Point Cloud Neighborhoods via Multi-Scale Geometric Measures
Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computer graphics or remote sensing. Algorithms typically operate in local neighborhoods of point sets, for computing physical states, surface reconstructions, etc. We present a visualization technique based on multi-scale geometric features of such point clouds. We explore properties of different choices on the underlying weighted co-variance neighborhood descriptor, illustrated on different point set geometries and for varying noise levels. The impact of different weighting functions and tensor centroids, as well as point set features and noise levels becomes visible in the rotation-invariant feature images. We compare to a curvature based scale space visualization method and, finally, show how features in real-world LiDAR data can be inspected by images created with our approach in an interactive tool. In contrast to the curvature based approach, with our method line structures are highlighted over growing scales, with clear border regions to planar or spherical geometric structures. CCS Concepts • Human-centered computing → Visual analytics; • Computing methodologies → Point-based models;
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