Research on a Camera Position Measurement Method based on a Parallel Perspective Error Transfer Model

TL;DR

This paper introduces a geometric error propagation framework for camera pose estimation using parallel perspective approximation, improving robustness and accuracy in near-field scenarios with heterogeneous noise.

Contribution

It presents a novel error transfer model and a pose estimation method that incorporates parallel perspective initialization and error-aware weighting within a Gauss-Newton scheme.

Findings

Achieves accuracy comparable to state-of-the-art methods

Demonstrates robustness in challenging lighting and underwater conditions

Maintains high computational efficiency

Abstract

Camera pose estimation from sparse correspondences is a fundamental problem in geometric computer vision and remains particularly challenging in near-field scenarios, where strong perspective effects and heterogeneous measurement noise can significantly degrade the stability of analytic PnP solutions. In this paper, we present a geometric error propagation framework for camera pose estimation based on a parallel perspective approximation. By explicitly modeling how image measurement errors propagate through perspective geometry, we derive an error transfer model that characterizes the relationship between feature point distribution, camera depth, and pose estimation uncertainty. Building on this analysis, we develop a pose estimation method that leverages parallel perspective initialization and error-aware weighting within a Gauss-Newton optimization scheme, leading to improved robustness in proximity operations. Extensive experiments on both synthetic data and real-world images, covering diverse conditions such as strong illumination, surgical lighting, and underwater low-light environments, demonstrate that the proposed approach achieves accuracy and robustness comparable to state-of-the-art analytic and iterative PnP methods, while maintaining high computational efficiency. These results highlight the importance of explicit geometric error modeling for reliable camera pose estimation in challenging near-field settings.