Abstract

Recent advances have enabled the use of modern remote sensing technologies to create high-resolution 3 D point clouds that capture the in-situ geometry of infrastructure systems. However, there is a need for new methods of analyzing and leveraging these complex data types. In this paper, the authors present an approach to quantifying textural deterioration and surficial damages manifested in point cloud data, such as corrosion or spall. This is achieved through geometric analysis of a nonlinear projection of the original color-space, and results in an algorithm that is generalizable to a variety of structural defects. The behavior of this algorithm is illustrated through both laboratory and field-scale experimental analysis, in which point cloud colorimetric differentials are identified automatically via the color-space damage detection algorithm and then compared with pixel-wise ground truth measurements. Overall measurement errors were on the order of 5-10%, with most error resulting from surface staining and surface reflectivity.

摘要

最近的进展使得能够使用现代遥感技术来创建高分辨率的 3D 点云，以捕捉基础设施系统的原位几何形状。然而，需要新的方法来分析和利用这些复杂的数据类型。在本文中，作者提出了一种量化点云数据中表现出的纹理恶化和表面损伤的方法，例如腐蚀或剥落。这是通过对原始色彩空间的非线性投影进行几何分析来实现的，并产生一种可推广到各种结构缺陷的算法。该算法的行为通过实验室和现场规模的实验分析来说明，其中点云色度差异通过颜色空间损伤检测算法自动识别，然后与像素级地面实况测量值进行比较。总体测量误差约为 5-10%，其中大部分误差是由表面染色和表面反射率造成的。