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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Updated: Jan 13, 2026

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Are Minimal Radial Distortion Solvers Really Necessary for Relative Pose Estimation?

Viktor Kocur1, Charalambos Tzamos2, Yaqing Ding2

  • 1Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava, Bratislava, Slovakia.

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Complex minimal solvers for radial distortion are unnecessary for camera relative pose estimation. Simple methods, like parameter sampling or neural networks, offer comparable performance with reduced complexity.

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Area of Science:

  • Computer Vision
  • Robotics
  • Photogrammetry

Background:

  • Estimating relative camera pose is crucial for Structure-from-Motion and other applications.
  • Standard methods use minimal solvers within RANSAC loops, but often neglect radial distortion.
  • Ignoring radial distortion significantly degrades pose estimation accuracy.

Purpose of the Study:

  • To evaluate simpler alternatives to complex minimal solvers for radial distortion in relative pose estimation.
  • To compare a sampling-based undistortion approach with a learning-based method.
  • To determine when simpler methods outperform traditional complex solvers.

Main Methods:

  • Compared efficient pinhole solvers with sampled radial undistortion parameters.
  • Evaluated a neural network approach for estimating distortion parameters.
  • Conducted extensive experiments on diverse datasets and camera setups.

Main Results:

  • Complex minimal radial distortion solvers are not essential in practice.
  • Simpler methods achieve comparable results to complex solvers.
  • Performance depends on specific dataset and camera configurations.

Conclusions:

  • Effective relative pose estimation under radial distortion can be achieved without complex minimal solvers.
  • Simple radial undistortion parameter sampling is often preferable to learning-based calibration.
  • The study provides a benchmark and code for further research.