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SuperNCN: Neighbourhood Consensus Network for Robust Outdoor Scenes Matching

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12002)

Abstract

In this paper, we present a framework for computing dense keypoint correspondences between images under strong scene appearance changes. Traditional methods, based on nearest neighbour search in the feature descriptor space, perform poorly when environmental conditions vary, e.g. when images are taken at different times of the day or seasons. Our method improves finding keypoint correspondences in such difficult conditions. First, we use Neighbourhood Consensus Networks to build spatially consistent matching grid between two images at a coarse scale. Then, we apply Superpoint-like corner detector to achieve pixel-level accuracy. Both parts use features learned with domain adaptation to increase robustness against strong scene appearance variations. The framework has been tested on a RobotCar Seasons dataset, proving large improvement on pose estimation task under challenging environmental conditions.

Keywords

  • Feature matching
  • Pose estimation
  • Domain adaptation

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  • DOI: 10.1007/978-3-030-40605-9_42
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Correspondence to Jacek Komorowski .

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Kurzejamski, G., Komorowski, J., Dabala, L., Czarnota, K., Lynen, S., Trzcinski, T. (2020). SuperNCN: Neighbourhood Consensus Network for Robust Outdoor Scenes Matching. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_42

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  • DOI: https://doi.org/10.1007/978-3-030-40605-9_42

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