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

2026-02-08

AI Summary

This work addresses the degraded stability of conventional Perspective-n-Point (PnP) methods in near-field scenarios, where strong perspective effects and heterogeneous measurement noise adversely impact performance. To this end, the authors propose a geometric error propagation framework based on a para-perspective approximation. By explicitly modeling the propagation of image measurement errors through perspective projection, the approach reveals the interplay among feature point distribution, camera depth, and pose estimation uncertainty. An error-aware weighting strategy is then integrated into a Gauss–Newton optimization scheme to achieve robust and efficient pose estimation. Extensive experiments on both synthetic and real-world images—including challenging conditions such as strong illumination, surgical lighting, and underwater low-light environments—demonstrate that the proposed method matches or exceeds the accuracy and robustness of state-of-the-art PnP algorithms while maintaining high computational efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

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.

Problem

• camera pose estimation
• near-field scenarios
• perspective effects
• measurement noise
• PnP

Innovation

• parallel perspective
• error propagation
• camera pose estimation
• PnP
• geometric error modeling