An efficient reconfigurable architecture to implement dense stereo vision algorithm using high-level synthesis

This article presents a reconfigurable architecture to calculate a dense disparity map of two stereo images based on census transform. This architecture is simplified and efficient as a result of binary operations and integer arithmetic used by census transform. Our architecture was prototyped using GAUT which is a practical tool to develop high-level synthesis. We take advantage of GAUT rapid prototyping to implement different architectures and to make a general comparison among them, that lets us to optimize the architecture, and at the same time, to improve the system's performance. The optimization and the resource saving rend our architecture an interesting option to solve the problem of stereo vision in real time, quite used in autonomous navigation.