Real-time Stereo Matching Using GPU Programming and Hierarchical Method

??? ????????? ????????? ??????????????? ???????????? ?????? PC ???????????? ??????????????? ???????????? ????????? ????????? ???????????? ???????????????????????????. ??? ??????????????? ????????? ????????? ????????? ????????? ?????? ????????? ??????????????? ????????? ??????????????? ????????? ?????????????????????????????? ??????????????? ???????????? ???????????? ????????? ???????????? ????????? ????????? ????????? ???????????? ????????????. ?????? ???????????? ???????????? ????????? ????????????(rank transform)??? ????????? ?????????????????? ??????????????? ???????????? ??? ???????????? ????????? ?????? ????????? ????????? ??????????????? ???????????????. ???????????? ?????????????????? ?????? ????????? ???????????? ????????? ?????? ?????? ????????? ???????????? ???????????????????????? ????????? ???????????? ???????????? ???????????? ???????????? ????????? ????????? ???????????? ????????? ??????????????? ???????????????. ?????????????????? ????????? ????????? ???????????? ????????????. ??? ???????????? ???????????? ???????????? ?????? ?????? ???????????? ?????????????????? ????????? ???????????? ??? ??????????????? ?????? ???????????? ????????? ??? ????????? ??????????????? ????????? ????????? ???????????????. ??????????????? ????????? ?????? ????????? ??????????????? ???????????? ????????? ??????????????? ?????? ????????? ??????????????? ????????????. Middlebury ???????????? ????????? ???????????? ????????? ????????? ?????? ??? ???????????? ????????? ??????????????? 640x480, 60?????? ??????????????? ???????????? 30?????????/??? ????????? ????????? ?????? ?????? ????????? ??????????????? ?????? ?????? ??????????????? ???????????? ????????????. ??? ???????????? ????????? ??????????????? ????????? ???????????? ??????????????? 3?????? ???????????? ??? ????????? ?????? ????????? ????????? ????????? ??? ?????? ????????? ????????????.This paper proposes a real-time stereo matching algorithm using GPU programming. The fast algorithms are implemented using GPU programming for real-time operation, and adaptive energy function is modeled for improving the accuracy of disparity estimation. First, the likelihood matching based on rank transform is implemented in GPU programming. The adaptive memory handling in graphic hardware is introduced in aggregating the matching errors. Once an initial disparity map is determined based on the likelihood matching, then the disparity map is iteratively updated by the prior model of disparity field. The prior model reflects the smoothness of disparity map and is implemented by a pixel-wise energy function. The disparity is determined by minimizing the joint energy function which combines the likelihood matching with the prior energy model. These processes are performed in the hierarchical approach. The disparity map is interpolated using color-based weights. This paper evaluates the proposed approach with the Middlebury stereo images. According to the experiments, the proposed method shows good estimation accuracy with more than 30 frame/second for 640x480 images. The proposed method is suitable for real-time stereo system in the usual PC environment