Joint modeling and cross-iterative calibration for multi-source systematic errors in ToF imaging systems

Time-of-flight (ToF) imaging systems have become an indispensable technology in three-dimensional (3D) measurement applications, owing to their high frame rate, robust performance, and cost-effectiveness. However, prevalent multi-source coupled systematic errors can significantly degrade their measurement accuracy and imaging quality. To address this challenge, this paper proposes a joint modeling and cross-iterative calibration method for multi-source systematic errors in ToF imaging systems. This method comprises a three-stage strategy. First, to address the complex synergistic effects of various error sources, a unified error model is constructed from raw differential correlation sampling (DCS) data, explicitly formulating the physical coupling among wiggling, geometric, and non-uniformity errors. Second, a hierarchical constraint strategy is proposed to systematically isolate these parameters, yielding reliable initial estimates that prevent the multi-parameter optimization from trapping into local minima. Finally, a cross-iterative global optimization, integrating discrete orthogonal search with sub-pixel gradient descent, is employed to accurately and efficiently resolve the optimal parameters for the coupled model. The effectiveness of the proposed method is verified through both laboratory experiments and real-world scenario validations. Compared with the representative existing methods, this method achieves the lowest root mean square error (RMSE) and the highest peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) on the tested platform.