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Robust Semi-Dense Matching across Uncalibrated and Widely Separated Views

This paper proposes an iterative framework to obtain a semi-dense matching from two wide-baseline images, when camera parameters and positions are unknown. The matching process is computed in three steps. First, a small number of robust correspondences is selected with SIFT descriptor. Then, this initial set is multiplied by iteratively reinforcing the epipolar constraint on rectified images during the estimation of the fundamental matrix. At a final refinement, point correspondences are boosted by applying a local affine transform in the image. The method has been tested on several pairs of images. Between three to four thousands points are paired on medium resolution images (1024x768 pixels) by this matching method: a sufficient number for accurate volumetric measurement.