OSPC: Online Sequential Photometric Calibration

Abstract

Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Additionally, it proves valuable in extracting sensor irradiance values from measured intensities, serving as a pre-processing step for a number of vision algorithms such as shape-from-shading. Current photometric calibration systems rely on a joint optimization problem and encounter an ambiguity in the estimates, which can only be resolved using ground truth information. We propose a novel method that solves for photometric parameters using a sequential estimation approach. To enhance the decoupling of CRF and Vignette estimation, we strategically utilize keyframes with high exposure ratios and small displacements for the former, and keyframes with relatively large displacements for the latter. Our proposed method achieves high accuracy in estimating all parameters; furthermore, the formulations are linear and convex, which makes the solution fast and suitable for online applications. Experiments on a Visual Odometry system validate the proposed method and demonstrate its advantages.