Minimal Solvers

The purpose of the minimal solvers is to efficiently solve polynomial systems that arise in Computer Vision.

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Why it is Important

  • Polynomial systems arise various Computer Vision tasks, such as 3D reconstruction, Visual Localization, SLAM, and Camera Calibration.
  • Often, the input data is comtaminated with noise and wrong values, so we need a robust solution.
  • To achieve robustness, minimal solvers must efficiently process many samples, which demands speed and efficiency.
  • The pursuit of minimal solvers embodies an intriguing approach to these complex problems.
  • Minimal solvers are cool. ☺

☺ Minimal Solvers are cool! ☺ ☺ Minimal Solvers are cool! ☺

Key Problems

  1. Relative Pose Estimation: Finding the pose between two or more cameras.

  2. Absolute Pose Estimation: Finding the pose between a camera and a point-cloud.

  3. Applications: Minimal solvers are used in many computer vision tasks, such as:

    • 3D reconstruction
    • Visual Localization
    • SLAM (Simultaneous Localization and Mapping)
    • Camera (Auto-)calibration

Conclusion

Minimal solvers are a very cool approach to solve polynomial systems that arise in Computer Vision. They are present in various computer vision pipelines.

☺ Minimal Solvers are cool! ☺ ☺ Minimal Solvers are cool! ☺

Publications

  • Handbook on Leveraging Lines for Two-View Relative Pose Estimation (3DV 2024) [Paper]
  • Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction (ICCV 2023) [Project page]
  • Four-view geometry with unknown radial distortion (CVPR 2023) [Paper]
  • Learning to Solve Hard Minimal Problems (CVPR 2022) [Paper]