Guiding Local Feature Matching with Surface Curvature

Shuzhe Wang, Juho Kannala, M. Pollefeys, Dániel Baráth
2/14/2026

Abstract

We propose a new method, called curvature similarity extractor (CSE), for improving local feature matching across images. CSE calculates the curvature of the local 3D surface patch for each detected feature point in a viewpoint-invariant manner via fitting quadrics to predicted monocular depth maps. This curvature is then leveraged as an additional signal in feature matching with off-the-shelf matchers like SuperGlue and LoFTR. Additionally, CSE enables end-to-end joint training by connecting the matcher and depth predictor networks. Our experiments demonstrate on large-scale real-world datasets that CSE consistently improves the accuracy of state-of-the-art methods. Fine-tuning the depth prediction network further enhances the accuracy. The proposed approach achieves state-of-the-art results on the ScanNet dataset, showcasing the effectiveness of incorporating 3D geometric information into feature matching.1

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Cite this paper

@article{wang2026guiding,
  title  = {Guiding Local Feature Matching with Surface Curvature},
  author = {Shuzhe Wang and Juho Kannala and M. Pollefeys and Dániel Baráth},
  year   = {2026},
  doi    = {10.1109/ICCV51070.2023.01648},
  url    = {https://doi.org/10.1109/ICCV51070.2023.01648},
  journal = {ICCV 2023 2023}
}

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