Atmospheric visibility distance is a property of the atmosphere, which can be remotely sensed by computer vision. In this aim, a non-linear mapping function between the atmospheric visibility distance and the contrast in images must be estimated. The function depends on the scene depth distribution as well as on the radiometry of the scene. In order to calibrate and deploy such camera-based atmospheric visibility estimations, we present two methods which aim at computing the scene depth distribution and the radiometry of the scene beforehand. The scene depth is recovered by registering a full 3D model of the environment in the frame of the camera. The radiometry of the scene is partly recovered by looking at the temporal correlation between the variation of pixels intensity and the variation of the sky luminance estimated by a luminance meter oriented toward the North direction. Based on clear-sky models, it is demonstrated that such a process detects a set of pixels, which include pixels belonging to North-oriented Lambertian surfaces. This finding leads to a simplified way of detecting Lambertian surfaces without any additional luminance meter. Good results obtained experimentally prove that such techniques are relevant to estimate the atmospheric visibility distance.
