A census-based stereo matching algorithm with multiple sparse windows

Stereo matching is one of the most active research areas in intelligent vehicle technology. In order to apply the stereo matching to intelligent vehicles, it must generate high-accuracy three-dimensional information in real time. For real-time stereo matching, this paper proposes a sparse multi-window method which not only gives robustness to noise but also reduces hardware cost with high matching accuracy. This is achieved by reusing the result of overlapped operations between adjacent windows. The experimental results show that the proposed method can reduce the hardware complexity of stereo matching processors with higher accuracy compared with the conventional window method.