A comparative study on the application of SIFT, SURF, BRIEF and ORB for 3D surface reconstruction of electronmicroscopy images

Image feature detector and descriptor algorithms have made a big advance in almost every area of computer vision applications including object localisation, object tracking, mobile robot mapping, watermarking, panorama stitching and 3D surface reconstruction by assisting the detection and description of feature points in a set of given images. In this paper, we evaluate the performance of four robust feature detection algorithms namely SIFT, SURF, BRIEF and ORB on multi-view 3D surface reconstruction of microscopic samples obtained by a scanning electron microscope (SEM), a widely used equipment in biological and materials sciences for determining the surface attributes of micro objects. To this end, we first develop an optimised multi-view framework for SEM extrinsic calibration and its 3D surface reconstruction. We design a Differential Evolutionary-based algorithm to solve the problem in a global optimisation platform. Several qualitative and quantitative comparisons such as reliability on SEM extrinsic calibration and validity on 3D visualisation performed on real microscopic objects as well as a synthetic model. The present evaluation is expected to provide better insights and consideration to determine which algorithm is well deserved for multi-view 3D SEM surface reconstruction.