Geometry Estimation of Urban Street Canyons Using Stereo Vision from Egocentric View

We investigate the problem of estimating the local geometric scene structure of urban street canyons captured from an egocentric viewpoint with a small-baseline stereo camera setup. We model the facades of buildings as planar surfaces and estimate their parameters based on a dense disparity map as only input. After demonstrating the importance of considering the stereo reconstruction uncertainties, we present two approaches to solve this model-fitting problem. The first approach is based on robust planar segmentation using random sampling, the second approach transforms the disparity into an elevation map from which the main building orientations can be obtained. We evaluate both approaches on a set of challenging inner city scenes and show how visual odometry can be incorporated to keep track of the estimated geometry in real-time.