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We address the semi-calibrated relative pose estimation problem where we assume the principal point to be located in the center of the image and estimate the focal lengths, relative rotation, and translation of two cameras. We introduce the first minimal solver that requires only a single affine correspondence in conjunction with predicted monocular depth. Recognizing its degeneracy when the correspondence stems from a fronto-parallel plane, we present an alternative solver adept at automatically recovering the correct solution under such circumstances. By integrating these methods within the GC-RANSAC framework, we show they surpass standard approaches, delivering more accurate poses and focal lengths at comparable runtimes across large-scale, publicly available indoor and outdoor datasets. The code is available at https://github.com/petrhruby97/semicalibrated_1AC_D .

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Semicalibrated Relative Pose from an Affine Correspondence and Monodepth

  • 会议论文

  • 发表日期：

    2025年

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  • Petr Hruby,
  • Marc Pollefeys,
  • Daniel Barath

摘要

We address the semi-calibrated relative pose estimation problem where we assume the principal point to be located in the center of the image and estimate the focal lengths, relative rotation, and translation of two cameras. We introduce the first minimal solver that requires only a single affine correspondence in conjunction with predicted monocular depth. Recognizing its degeneracy when the correspondence stems from a fronto-parallel plane, we present an alternative solver adept at automatically recovering the correct solution under such circumstances. By integrating these methods within the GC-RANSAC framework, we show they surpass standard approaches, delivering more accurate poses and focal lengths at comparable runtimes across large-scale, publicly available indoor and outdoor datasets. The code is available at https://github.com/petrhruby97/semicalibrated_1AC_D .