SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples.
Scanning electron microscopy (SEM) imaging has been a principal component of many studies in biomedical, mechanical, and materials sciences since its emergence. Despite the high resolution of captured images, they remain two-dimensional (2D). In this work, a novel framework using sparse-dense correspondence is introduced and investigated for 3D reconstruction of stereo SEM images. SEM micrographs from microscopic samples are captured by tilting the specimen stage by a known angle. The pair of SEM micrographs is then rectified using sparse scale invariant feature transform (SIFT) features/descriptors and a contrario RANSAC for matching outlier removal to ensure a gross horizontal displacement between corresponding points. This is followed by dense correspondence estimation using dense SIFT descriptors and employing a factor graph representation of the energy minimization functional and loopy belief propagation (LBP) as means of optimization. Given the pixel-by-pixel correspondence and the tilt angle of the specimen stage during the acquisition of micrographs, depth can be recovered. Extensive tests reveal the strength of the proposed method for high-quality reconstruction of microscopic samples.
Department of Electrical Engineering, University of Wisconsin-Milwaukee, WI, USA. Electronic address: abaghaie@uwm.edu.
Ahmadreza
A
Baghaie
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, WI, USA.
Ahmad P
AP
Tafti
Department of Biological Sciences, University of Wisconsin-Milwaukee, WI, USA.
Heather A
HA
Owen
Department of Mechanical Engineering, University of Wisconsin-Milwaukee, WI, USA.
Roshan M
RM
D'Souza
Departments of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, WI, USA.
Zeyun
Z
Yu
3D reconstruction; Dense correspondence; Feature descriptors; Scanning electron microscope (SEM)