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Curvilinear structures frequently appear in microscopy imaging as the objectof interest. Crystallographic defects, i.e., dislocations, are one of thecurvilinear structures that have been repeatedly investigated undertransmission electron microscopy (TEM) and their 3D structural information isof great importance for understanding the properties of materials. 3Dinformation of dislocations is often obtained by tomography which is acumbersome process since it is required to acquire many images with differenttilt angles and similar imaging conditions. Although, alternative stereoscopymethods lower the number of required images to two, they still require humanintervention and shape priors for accurate 3D estimation. We propose a fullyautomated pipeline for both detection and matching of curvilinear structures instereo pairs by utilizing deep convolutional neural networks (CNNs) withoutmaking any prior assumption on 3D shapes. In this work, we mainly focus on 3Dreconstruction of dislocations from stereo pairs of TEM images.

3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks