sWMF: Separable weighted median filter for efficient large-disparity stereo matching
Shiqiang Chen, Xuchong Zhang, Hongbin Sun, Nanning Zheng · 2017
Although large disparity stereo matching is critical to the practical application of stereo vision system especially for outdoor scenes, its efficient hardware design is still a grand challenge. Motivated by the discovery that well-designed weighted median filter (WMF) can achieve satisfactory accuracy with simple box-filter aggregation, this paper proposes a separable weighted median filter (sWMF) that only has the computational complexity of O(r) and is independent of disparity range. Moreover, the proposed sWMF can be efficiently implemented as a fully pipelined architecture. Evaluation results demonstrate that, at the penalty of only 0.06% disparity error rate, the proposed sWMF design can save 12.9% Slice LUTs, 76.7% DSPs and 64.0% Block RAMs at the disparity range of 128, compared with previous WMF implementation on FPGA.