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.

中文翻译：

---

OSPC：在线顺序光度校准

光度校准对于许多计算机视觉应用至关重要。其主要优点之一是增强 Visual SLAM 的性能，特别是当它依赖于直接跟踪方法（例如标准 KLT 算法）时。此外，它在从测量的强度中提取传感器辐照度值方面被证明很有价值，可以作为许多视觉算法（例如阴影形状）的预处理步骤。当前的光度校准系统依赖于联合优化问题，并且在估计中遇到模糊性，只能使用地面实况信息来解决。我们提出了一种使用顺序估计方法求解光度参数的新颖方法。为了增强CRF和Vignette估计的解耦，我们策略性地利用前者的高曝光率和小位移的关键帧，以及后者的相对大位移的关键帧。我们提出的方法在估计所有参数方面实现了高精度；此外，该公式是线性和凸的，这使得解决方案快速并且适合在线应用。视觉里程计系统的实验验证了所提出的方法并展示了其优势。