Rolling Shutter Relative Pose Estimation Made Practical

Rolling shutter (RS) cameras equip virtually all consumerdevices, yet RS-aware relative pose estimation has remained impracti-cal: the state-of-the-art solver requires a minimum of 20 point corre-spondences, making RANSAC-based robust estimation prohibitively ex-pensive due to the exponential dependence of the iteration count onthe sample size. We make RS relative pose estimation practical by in-troducing affine correspondences (ACs) into the RS two-view geome-try. We derive novel RS-corrected affine constraints that account forthe coupling between point perturbations and the row-dependent es-sential matrix, providing two equations per correspondence beyond thestandard epipolar constraint. Building on these constraints, we developa linearized algebraic solver that estimates pose and RS motion fromonly 7 ACs. The solver exploits the physical smallness of RS parametersto linearize the constraints, eliminates the 12 RS unknowns via null-space projection, and solves the remaining degree-20 system via actionmatrices in 1.2 ms. On the TUM RS benchmark, our method achievesthe best pose and RS parameter accuracy among all tested methodsand, uniquely among RS solvers, provides accurate translational veloc-ity estimates – which are poorly conditioned from point correspondencesalone due to a v-t coupling. On the global-shutter EuRoC MAV dataset,the solver achieves comparable accuracy to the standard 5-point algo-rithm, demonstrating that it generalizes well to the GS setting. Code isat https://github.com/danini/rollingshuttermade_practical.