Geometry-Aware Feature Matching for Structure from Motion Applications

We present a two-stage, geometry-aware approach for matching SIFT-like features in a fast and reliable manner. Our approach first uses a small sample of features to estimate the epipolar geometry between the images and leverages it for guided matching of the remaining features. This simple and generalized two-stage matching approach produces denser feature correspondences while allowing us to formulate an accelerated search strategy to gain significant speedup over the traditional matching. The traditional matching punitively rejects many true feature matches due to a global ratio test. The adverse effect of this is particularly visible when matching image pairs with repetitive structures. The geometry-aware approach prevents such pre-emptive rejection using a selective ratio-test and works effectively even on scenes with repetitive structures. We also show that the proposed algorithm is easy to parallelize and implement it on the GPU. We experimentally validate our algorithm on publicly available datasets and compare the results with state-of-the-art methods.