The purpose of the minimal solvers is to efficiently solve polynomial systems that arise in Computer Vision.
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. ☺
Relative Pose Estimation: Finding the pose between two or more cameras.
Absolute Pose Estimation: Finding the pose between a camera and a point-cloud.
Applications: Minimal solvers are used in many computer vision tasks, such as:
- 3D reconstruction
- Visual Localization
- SLAM (Simultaneous Localization and Mapping)
- Camera (Auto-)calibration
Minimal solvers are a very cool approach to solve polynomial systems that arise in Computer Vision. They are present in various computer vision pipelines.
- 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]