Depth Estimation from the Color Drift of a Route Panorama

Image based modeling methods has been well studied for generating a 3D model from an image sequence. Most of them require redundant and huge spatio-temporal images for estimating a scene depth. It is not good characteristic for taking a higher resolution of texture. A route panorama is a continuous panoramic image along a path. It is suitable for modeling large environments such as a city or town. The panorama captured by a tine scan sensor also has advantage for capturing higher resolution easily. In this paper, we propose a method for depth estimation from the panorama. The route panorama has color drifts that correspond to the distances of captured objects. We use these color drifts to estimate the depth of an image. The proposed method detects the color drift by window matching using Belief Propagation. It also uses a Gaussian Pyramid to stabilize the estimation and decrease its computation cost. We confirmed that the proposed method estimated depth maps from a single high-resolution panorama in experiments.