Semi-global Matching Based on a Modified Census Transform with Gradient Maps

Abstract

Census transform has been identified to be very effective and robust for stereo matching. But it needs too much computation and the accuracy is limited. In this paper, a semi-global matching based on a modified census transform is proposed to decrease the computation. To take use of the parallel computation like CUDA, a new cost model is proposed by using the gradient maps. The proposed method performs better than the original method in the experiment. Compared to the original method, the proposed method reduces the time consumption while remaining the accuracy.