ABSTRACT
The scanning electron microscope (SEM), as one of the most commonly used instruments in biology and material sciences, employs electrons instead of light to determine the surface properties of specimens. However, the SEM micrographs still remain 2D images. To effectively measure and visualize the surface attributes, we need to restore the 3D shape model from the SEM images. 3D surface reconstruction is a longstanding topic in microscopy vision as it offers quantitative and visual information for a variety of applications consisting medicine, pharmacology, chemistry, and mechanics. In this paper, we attempt to explain the expanding body of the work in this area, including a discussion of recent techniques and algorithms. With the present work, we also enhance the reliability, accuracy, and speed of 3D SEM surface reconstruction by designing and developing an optimized multi-view framework. We then consider several real-world experiments as well as synthetic data to examine the qualitative and quantitative attributes of our proposed framework. Furthermore, we present a taxonomy of 3D SEM surface reconstruction approaches and address several challenging issues as part of our future work.
