A CNN solution for depth estimation from binocular stereo imagery

Abstract

Novel results and experiments are presented on the application of cellular neural networks to binocular stereo vision. A cellular neural network (CNN) universal machine (UM) algorithm is described for depth estimation as part of a stereo-vision-based guidance system for autonomous vehicles. Being most amenable to revealing stereo correspondence, extraction of vertical edges is performed first. Then their distance from the observer in 3D space is established through a stereo matching scheme. The performance of the algorithm is demonstrated on real-life highway imagery and it is shown that very low latency real-time operation is attainable via the CNN-UM.