High-precision visual imaging model and calibration method for multi-depth-of-field targets

In the field of high-precision large-depth 3D reconstruction, some bottlenecks exist in the improvement of visual measurement accuracy. This is because traditional vision measurement methods simplify the imaging model of a fixed-focus camera to a pinhole imaging model with fixed parameters accompanied by various distortion information. In fact, according to the optical properties of camera imaging, the position of the optical center in the pinhole imaging model varies with the depth of the imaging target. In this paper, in order to achieve the objective of improving visual measurement accuracy, a high-precision visual imaging model for multi-depth -of-field targets and the corresponding camera parameters calibration method are proposed based on the optical properties of camera imaging. When using this imaging model for 3D measure-ments, different pixel points in the image use different camera internal parameters depending on the depth of the imaging target, thus enabling highly accurate 3D reconstruction. The experi-mental results show that the imaging model and calibration method proposed in this paper greatly improve the accuracy and robustness of visual 3D measurement and have a promising future in the field of large-depth 3D reconstruction.

Automated summary

A high-precision visual imaging model and corresponding camera parameters calibration method are proposed in this paper, which can utilize different camera internal parameters based on the depth of the imaging target to achieve highly accurate 3D reconstruction. The experimental results show that the proposed imaging model and calibration method greatly improve the accuracy and robustness of visual 3D measurement, with promising future prospects in large-depth 3D reconstruction.