Keyframe-Based Photometric Online Calibration and Color Correction

Abstract

Finding the parameters of a vignetting function for a camera currently involves the acquisition of several images in a given scene under very controlled lighting conditions, a cumbersome and error-prone task where the end result can only be confirmed visually. Many computer vision algorithms assume photoconsistency, constant intensity between scene points in different images, and tend to perform poorly if this assumption is violated. We present a real-time online vignetting and response calibration with additional exposure estimation for global-shutter color cameras. Our method does not require uniformly illuminated surfaces, known texture or specific geometry. The only assumptions are that the camera is moving, the illumination is static and reflections are Lambertian. Our method estimates the camera view poses by sparse visual SLAM and models the vignetting function by a small number of thin plate splines (TPS) together with a sixth-order polynomial to provide a dense estimation of attenuation from sparsely sampled scene points. The camera response function (CRF) is jointly modeled by a TPS and a Gamma curve. We evaluate our approach on synthetic datasets and in real-world scenarios with reference data from a Structure-from-Motion (SfM) system. We show clear visual improvement on textured meshes without the need for extensive meshing algorithms. A useful calibration is obtained from a few keyframes which makes an on-the-fly deployment conceivable.