A Slit Scanning Depth of Route Panorama from Stationary Blur

Abstract

This work achieves an efficient acquisition of scenes and their depths along streets. During the movement of a vehicle, a slit in the camera frame is set properly to sample scenes continuously for a route panorama. This paper proposes a novel method of depth estimation by analyzing a new phenomenon named stationary blur in the route panorama. We find its relation with the depth and evaluate its degree at local and global levels. The depth estimation through filtering avoids feature matching and tracking that are error-prone in the scanning of real and complex street scenes. Our method provides reliable results but requires much less data than that of the structure from motion. This keeps the elegance of the route panorama in data representation, and is suitable for real time sensor development. Utilizing the completeness of the route panorama in the scene archiving, we can generate planar models of streets, which can be used in city visualization.