Siemens Stefan, Kästner Markus, Reithmeier Eduard
Institute of Measurement and Automatic Control, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany.
Data Brief. 2023 Mar 28;48:109094. doi: 10.1016/j.dib.2023.109094. eCollection 2023 Jun.
The dataset presented contains microtopographies of various materials and processing methods. These microtopographies were measured using a Confocal Laser Scanning Microscope, which provides RGB-D data. This means the dataset comprises accurate height maps for each measurement and microscopic RGB images. The height maps can be used to quantify and characterize small-scale surface features such as pits and grooves, surface roughness, texture direction, and surface anisotropy. These features can significantly impact a material's properties and behavior, making them essential in many fields, such as biomaterials and tribology. Additionally, the dataset contains metadata about the specimens and the measurement conditions, such as material, surface processing method, roughness, and optical magnification. Therefore, this dataset provides an opportunity to develop and test surface classification and characterization algorithms.
所呈现的数据集包含各种材料和加工方法的微观形貌。这些微观形貌是使用共聚焦激光扫描显微镜测量的，该显微镜提供RGB-D数据。这意味着数据集包括每次测量的精确高度图和微观RGB图像。高度图可用于量化和表征小规模表面特征，如凹坑和凹槽、表面粗糙度、纹理方向和表面各向异性。这些特征会显著影响材料的性能和行为，使其在生物材料和摩擦学等许多领域至关重要。此外，数据集包含有关样本和测量条件的元数据，如材料、表面加工方法、粗糙度和光学放大倍数。因此，该数据集为开发和测试表面分类及表征算法提供了机会。
