A multi-stage approach to the dense estimation of disparity from stereo SEM images

摘要

We are currently involved in an industrial project to recover depth information from stereo image pairs retrieved using a scanning electron microscope (SEM). Feature-based approaches to stereo provide accurate disparity estimations, however the quantity of estimates recovered is small (typically 1–2% of the image). If a continuous approximation to the surface is to be reconstructed, as requested by potential customers, more data has to be recovered. Our approach involves using the disparity estimates from a feature-based stereo algorithm to constrain a function fitting process. Assuming the image may be represented by an iterated facet model, the algorithm attempts to fit piecewise polynomials between the feature disparity estimates, which describe the mapping of grey-levels from the left to right image along epi-polars. The problems of illumination variation between the left and right images have been addressed using a modification to rankorder filtering which we call ‘soft’ ranking. Using a ‘B-fitting’ algorithm enables both the model order as well as the model parameters to be optimized. This is shown to improve the stability of the fitting process, when compared with a least-squares algorithm, by reducing the effective number of model parameters down to the minimum necessary in order to describe the data. The fitted functions are then used to calculate intermediate disparities, augmenting those recovered using stretch correlation. Finally a Laplace filter is used to close the surface.