Blair Jennifer, Stephen Bruce, Brown Blair, McArthur Stephen, Gorman David, Forbes Alistair, Pottier Claire, McAlorum Jack, Dow Hamish, Perry Marcus
Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK.
National Physical Laboratory, Teddington, TW11 0LW, UK.
Data Brief. 2024 Feb 6;53:110164. doi: 10.1016/j.dib.2024.110164. eCollection 2024 Apr.
Photometric stereo uses images of objects illuminated from various directions to calculate surface normals which can be used to generate 3D meshes of the object. Such meshes can be used by engineers to estimate damage of a concrete surface, or track damage progression over time to inform maintenance decisions. This dataset [1] was collected to quantify the uncertainty in a photometric stereo test rig through both the comparison with a well characterised method (coordinate measurement machine) and experiment virtualisation. Data was collected for 9 real objects using both the test rig and the coordinate measurement machine. These objects range from clay statues to damaged concrete slabs. Furthermore, synthetic data for 12 objects was created via virtual renders generated using Blender (3D software) [2]. The two methods of data generation allowed the decoupling of the physical rig (used to light and photograph objects) and the photometric stereo algorithm (used to convert images and lighting information into 3D meshes). This data can allow users to: test their own photometric stereo algorithms, with specialised data created for structural health monitoring applications; provide an industrially relevant case study to develop and test uncertainty quantification methods on test rigs for structural health monitoring of concrete; or develop data processing methodologies for the alignment of scaled, translated, and rotated data.
光度立体视觉利用从不同方向照亮物体的图像来计算表面法线，这些法线可用于生成物体的三维网格。工程师可以使用这样的网格来估计混凝土表面的损伤，或者跟踪损伤随时间的发展情况，以便做出维护决策。通过与一种特征明确的方法（坐标测量机）进行比较以及实验虚拟化，收集了该数据集[1]，以量化光度立体视觉测试装置中的不确定性。使用测试装置和坐标测量机为9个真实物体收集了数据。这些物体从黏土雕像到受损的混凝土板不等。此外，通过使用Blender（3D软件）[2]生成的虚拟渲染创建了12个物体的合成数据。这两种数据生成方法使得物理装置（用于照亮和拍摄物体）和光度立体视觉算法（用于将图像和光照信息转换为三维网格）得以解耦分离。这些数据可以让用户：使用为结构健康监测应用创建的专门数据来测试他们自己的光度立体视觉算法；提供一个与工业相关的案例研究，以开发和测试用于混凝土结构健康监测的测试装置上的不确定性量化方法；或者开发用于对齐缩放、平移和旋转数据的数据处理方法。
