Generalized Camera Calibration: Camera Model Selection and Calibration With Effective Image Sampling

Quy Nguyen Cong, Sunglok Choi

Abstract: Camera calibration is essential for accurate optical measurement and computer vision applications. The precision of calibration parameters significantly impacts the performance of subsequent computer vision tasks. However, the process is often complicated by the need to select the most suitable camera projection model, determine the optimal number of images, choose appropriate viewpoints, and define criteria for high-quality dataset construction. To address these challenges, we introduce the generalized camera calibration framework, a novel approach that automates dataset creation, considers various camera projection models, and identifies the optimal model and its parameters based on comprehensive model selection criteria. This framework streamlines the calibration process, eliminating the need for manual image and camera model selection before camera calibration. Our method demonstrates outstanding performance on both synthetic and real data. On synthetic datasets, it achieves a remarkable 93.18% accuracy in identifying the correct ground-truth (GT) model using 40 images, employing BIC for model selection. When applied to real datasets, our method maintains a consistent root-mean-square reprojection error (RMSE) of approximately 0.3 pixels across both training and test sets. Extensive validation on synthetic and real data underscores the significant performance enhancements achieved through our approach, making it a powerful tool for simplifying and improving camera calibration in various applications.

External IDs:doi:10.1109/jsen.2025.3581377