OSPC: Online Sequential Photometric Calibration
This work addresses photometric calibration for computer vision systems, offering an incremental improvement over existing methods by resolving ambiguity without ground truth.
The paper tackles the problem of photometric calibration in computer vision, which is crucial for improving Visual SLAM and other applications, by proposing a sequential estimation method that achieves high accuracy with linear and convex formulations for fast online use.
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. Another advantage could be in retrieving the sensor irradiance values from measured intensities, as a pre-processing step for some 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. 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.