Research
Fast Iterative Five point Relative Pose Estimation
arXiv:2608.13114v1 Announce Type: new Abstract: Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point
arXiv:2608.13114v1 Announce Type: new Abstract: Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nister [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell's Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister's algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.
Related
- PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertainty
- Fast Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation
- WildPose: A Unified Framework for Robust Pose Estimation in the Wild
- CIPER: A Unified Framework for Cross-view Image-retrieval and Pose-estimation
- DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity
Source: arXiv cs.CV | 2026-08-14