A Minimal Set of Parameters Based Depth-Dependent Distortion Model and Its Calibration Method for Stereo Vision Systems

TL;DR

This paper introduces a minimal parameter depth-dependent distortion model for stereo vision systems, simplifying calibration and improving accuracy, validated through experiments showing significant improvements over traditional models.

Contribution

The work proposes a new minimal set of parameters for depth-dependent distortion modeling and a flexible calibration method using planar patterns, enhancing accuracy and ease of calibration.

Findings

MDM improved calibration accuracy by over 56% compared to Li's model.

The calibration method is easier and more flexible than classical techniques.

Iteration-based reconstruction increased depth accuracy by 9%.

Abstract

Depth position highly affects lens distortion, especially in close-range photography, which limits the measurement accuracy of existing stereo vision systems. Moreover, traditional depth-dependent distortion models and their calibration methods have remained complicated. In this work, we propose a minimal set of parameters based depth-dependent distortion model (MDM), which considers the radial and decentering distortions of the lens to improve the accuracy of stereo vision systems and simplify their calibration process. In addition, we present an easy and flexible calibration method for the MDM of stereo vision systems with a commonly used planar pattern, which requires cameras to observe the planar pattern in different orientations. The proposed technique is easy to use and flexible compared with classical calibration techniques for depth-dependent distortion models in which the lens must be perpendicular to the planar pattern. The experimental validation of the MDM and its calibration method showed that the MDM improved the calibration accuracy by 56.55% and 74.15% compared with the Li's distortion model and traditional Brown's distortion model. Besides, an iteration-based reconstruction method is proposed to iteratively estimate the depth information in the MDM during three-dimensional reconstruction. The results showed that the accuracy of the iteration-based reconstruction method was improved by 9.08% compared with that of the non-iteration reconstruction method.