# Camera Pose Revisited

arXiv: https://arxiv.org/abs/2601.12567 · HTML: https://mlsanity.com/paper/2601.12567/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Deep ChArUco: Dark ChArUco Marker Pose Estimation](https://mlsanity.com/paper/1812.03247/) (2018)

## Built on by (most cited first, 1 of 1)

- [On RGB-TIR Stereo Calibration under Extreme Resolution Asymmetry](https://mlsanity.com/paper/2605.15860/) (2026)

## Abstract (arXiv)

Estimating the position and orientation of a camera with respect to an observed scene is one of the central problems in computer vision, particularly in the context of camera calibration and multi-sensor systems. This paper addresses the planar Perspective--n--Point problem, with special emphasis on the initial estimation of the pose of a calibration object. As a solution, we propose the textttPnP-ProCay78 algorithm, which combines the classical quadratic formulation of the reconstruction error with a Cayley parameterization of rotations and least-squares optimization. The key component of the method is a deterministic selection of starting points based on an analysis of the reconstruction error for two canonical vectors, allowing costly solution-space search procedures to be avoided. Experimental validation is performed using data acquired also from high-resolution RGB cameras and very low-resolution thermal cameras in an integrated RGB--IR setup. The results demonstrate that the proposed algorithm achieves practically the same projection accuracy as optimal textttSQPnP and slightly higher than textttIPPE, both prominent textttPnP-OpenCV procedures. However, textttPnP-ProCay78 maintains a significantly simpler algorithmic structure. Moreover, the analysis of optimization trajectories in Cayley space provides an intuitive insight into the convergence process, making the method attractive also from a didactic perspective. Unlike existing PnP solvers, the proposed textttPnP-ProCay78 algorithm combines projection error minimization with an analytically eliminated reconstruction-error surrogate for translation, yielding a hybrid cost formulation that is both geometrically transparent and computationally efficient.