Curved Alleyway Understanding Based on Monocular Vision in Street Scenes
Luping Wang, Hui Wei
Abstract
Delivery using autonomous vehicles for medical and emergency supplies is a potential way to avoid unsafe and unpredictable factors. However, its implementation is hindered due to several key issues. A major dilemma is understanding curved alleyways in street scenes. These can be seen as compositions of non-Manhattan structures, which can help us estimate their original posture in three-dimensional scenes. We propose a new methodology to understand curved alleyways, and to bridge the gap between two-dimensional scene understanding and three-dimensional environment reconstruction from a monocular camera. Angle projections are assigned to clusters. Coplanar surfaces, which can compose fold structures, are estimated. Curved alley scenes are approximately represented by Manhattan and non-Manhattan fold structures, and approximated in the reconstruction of alley scenes. With geometric features, the algorithm requires no prior training or knowledge of the camera’s internal parameters. We compared the estimated layout to the ground truth and measured the percentage of incorrectly classified pixels. The results showed that the algorithm can successfully understand alley scenes including both Manhattan and curved non-Manhattan structures.