# Virtual-point-based Solutions to Handle Generalized Absolute Pose Problem

arXiv: https://arxiv.org/abs/2606.09294 · HTML: https://mlsanity.com/paper/2606.09294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset](https://mlsanity.com/paper/2002.10152/) (2020)
- [Uncertainty-Aware Camera Pose Estimation from Points and Lines](https://mlsanity.com/paper/2107.03890/) (2021)
- [Large Scale SfM with the Distributed Camera Model](https://mlsanity.com/paper/1607.03949/) (2016)
- [gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors](https://mlsanity.com/paper/2004.02052/) (2020)
- [Accurate Pose Estimation for Flight Platforms based on Divergent Multi-Aperture Imaging System](https://mlsanity.com/paper/2502.19708/) (2025)
- [Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints](https://mlsanity.com/paper/2011.13817/) (2020)

## Abstract (arXiv)

Multi-camera systems are increasingly adopted in robotics and autonomous navigation for their wide field of view, flexibility, and fault tolerance. Nevertheless, existing PnP solvers fail to handle multiple projection centers. This paper introduces a virtual point formulation that bridges the standard PnP and generalized pose problems, enabling a unified pipeline that transforms existing PnP solvers into generalized pose solvers. Based on this framework, we derive three Virtual-point-based Generalized Pose solvers, namely VGPc, VGPq, and VGPr, leveraging Cayley, quaternion, and rotation-matrix parameterizations, respectively. Extensive experiments demonstrate that the proposed solvers inherit the accuracy and efficiency of original PnP algorithms while significantly outperforming existing generalized solvers. Specifically, VGPc achieves higher estimation accuracy under heteroscedastic noise conditions, VGPq maintains global optimality, whereas VGPr provides superior computational efficiency without accuracy degradation.