Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence

摘要

The stereo matching algorithms usually do not satisfy the performance of both accuracy and complexity. The semi-global matching (SGM) is the most efficient method among the stereo matching algorithms. However, it still does not utilize the whole image information to estimate the disparity values. In this paper, we proposed a new concept of the cost aggregation method using summed area table (SAT) scheme. The SAT is the efficient algorithm for summing intensities on the rectangular area in the image. Using four kinds of the SAT arrays (we called these arrays cost aggregation table (CAT)), it is possible to estimate the disparity values with aggregating every cost in the image. We tested our algorithm using the KITTI vision benchmark suite, the result shows that our algorithm is superior for disparity accuracy compared to the SGM. We expect that the CAT can be an alternative cost aggregation method to the SGM in the near future.