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A General Dense Image Matching Framework Combining Direct and Feature-Based Costs

Abstract : Dense motion field estimation (typically optical flow, stereo disparity and surface registration) is a key computer vision problem. Many solutions have been proposed to compute small or large displacements, narrow or wide baseline stereo disparity, but a unified methodology is still lacking. We here introduce a general framework that robustly combines direct and feature-based matching. The feature-based cost is built around a novel robust distance function that handles key points and ``weak'' features such as segments. It allows us to use putative feature matches which may contain mismatches to guide dense motion estimation out of local minima. Our framework uses a robust direct data term (AD-Census). It is implemented with a powerful second order Total Generalized Variation regularization with external and self-occlusion reasoning. Our framework achieves state of the art performance in several cases (standard optical flow benchmarks, wide-baseline stereo and non-rigid surface registration). Our framework has a modular design that customizes to specific application needs.
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Contributor : Vincent Gay-Bellile <>
Submitted on : Thursday, July 12, 2018 - 1:28:38 PM
Last modification on : Monday, February 10, 2020 - 6:13:47 PM

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Jim Braux-Zin, Romain Dupont, Adrien Bartoli. A General Dense Image Matching Framework Combining Direct and Feature-Based Costs. IEEE International Conference on Computer Vision (ICCV), Dec 2013, Sydney, Australia. ⟨10.1109/ICCV.2013.30⟩. ⟨cea-01836523⟩



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