Dense Disparity Estimation via Global and Local Matching

A new divide-and-conquer technique for disparity estimation is proposed in this paper. This technique performs feature matching recursively, starting with the strongest feature point in the left scanline. Once the first matching pair is established, the ordering constraint in disparity estimation allows the original intra-scanline matching problem to be divided into two smaller subproblems. Each subproblem can then be solved recursively, or via a disparity space technique. An extension to the standard disparity space technique is also proposed to compliment the divide-and-conquer algorithm. Experimental results demonstrate the effectiveness of the proposed approaches.