The Scanning Electron Microscope (SEM) as 2D imaging instrument has been widely used in biological, mechanical, and materials sciences to determine the surface attributes (e.g., compositions or geometries) of microscopic specimens. A SEM offers an excellent capability to overcome the limitation of human eyes by achieving increased magnification, contrast, and resolution greater than 1 nanometer. However, SEM micrographs still remain two-dimensional (2D). Having truly three-dimensional (3D) shapes from SEM micrographs would provide anatomic surfaces allowing for quantitative measurements and informative visualization of the objects being investigated. In biology, for example, 3D SEM surface reconstructions would enable researchers to investigate surface characteristics and recognize roughness, flatness, and waviness of a biological structure. There are also various applications in material and mechanical engineering in which 3D representations of material properties would allow us to accurately measure a fractal dimension and surface roughness and design a micro article which needs to fit into a tiny appliance. 3D SEM surface reconstruction employs several computational technologies, such as multi-view geometry, computer vision, optimization strategies, and machine learning to tackle the inverse problem going from 2D to 3D. In this contribution, an attempt is made to provide a 3D microscopy dataset along with the underlying algorithms publicly and freely available at http://selibcv.org/3dsem/ for the research community.