Charalambos Tzamos presents Are Minimal Radial Distortion Solvers Really Necessary for Relative Pose Estimation?
On 2026-05-25 11:30:00 at G205, Karlovo náměstí 13, Praha 2
** This seminar was postponed from May 19th to May 25th **
Estimating the relative pose between two cameras is a fundamental step in many
applications such as Structure-from-Motion. The common approach to relative
pose estimation is to apply a minimal solver inside a RANSAC loop. Highly
efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit
radial distortion. Not modeling radial distortion leads to (significantly)
worse results. However, minimal radial distortion solvers are significantly
more
complex than pinhole solvers, both in terms of run-time and implementation
efforts. This paper compares radial distortion solvers with two
simple-to-implement approaches that do not use minimal radial distortion
solvers: The first approach combines an efficient pinhole solver with sampled
radial undistortion parameters, where the sampled parameters are used for
undistortion prior to applying the pinhole solver. The second approach uses a
state-of-the-art neural network to estimate the distortion parameters rather
than sampling them from a set of potential values. Extensive experiments on
multiple datasets, and different camera setups, show that complex minimal
radial distortion solvers are not necessary in practice. We discuss under which
conditions a simple sampling of radial undistortion parameters is preferable
over calibrating cameras using a learning-based prior approach. Code and newly
created benchmark for relative pose estimation under radial distortion are
available at https://github.com/kocurvik/rdnet.
Paper link: https://link.springer.com/article/10.1007/s11263-025-02657-3
Estimating the relative pose between two cameras is a fundamental step in many
applications such as Structure-from-Motion. The common approach to relative
pose estimation is to apply a minimal solver inside a RANSAC loop. Highly
efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit
radial distortion. Not modeling radial distortion leads to (significantly)
worse results. However, minimal radial distortion solvers are significantly
more
complex than pinhole solvers, both in terms of run-time and implementation
efforts. This paper compares radial distortion solvers with two
simple-to-implement approaches that do not use minimal radial distortion
solvers: The first approach combines an efficient pinhole solver with sampled
radial undistortion parameters, where the sampled parameters are used for
undistortion prior to applying the pinhole solver. The second approach uses a
state-of-the-art neural network to estimate the distortion parameters rather
than sampling them from a set of potential values. Extensive experiments on
multiple datasets, and different camera setups, show that complex minimal
radial distortion solvers are not necessary in practice. We discuss under which
conditions a simple sampling of radial undistortion parameters is preferable
over calibrating cameras using a learning-based prior approach. Code and newly
created benchmark for relative pose estimation under radial distortion are
available at https://github.com/kocurvik/rdnet.
Paper link: https://link.springer.com/article/10.1007/s11263-025-02657-3