Hybrid tree guided PatchMatch and quantizing acceleration for multiple views disparity estimation  

作　　者：张吉光 徐士彪 张晓鹏 ZHANG Jiguang;XU Shibiao;ZHANG Xiaopeng(National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing,China)

机构地区：[1]National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing,China

出　　处：《中国体视学与图像分析》2021年第1期47-61,共15页Chinese Journal of Stereology and Image Analysis

基　　金：国家重点研究发展计划(No.2018YFB2100602);国家自然科学基金(No.6162010603,91646207,61971418,61771026,61972459)。

摘　　要：Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for ihe disparity estimation of large-scale or multi-view images.Hybrid tree method can obtain a disparity estimation fast with relatively low accuracy,while PatchMatch can give high-precision disparity value with relatively high computational cost.In this work,we propose the Hybrid Tree Guided PatchMatch which can calculate the disparity fast and accurate.Firstly,an initial disparity map is estimated by employing hybrid tree cost aggregation,which is used to constrain the label searching range of the PatchMatch.Furthermore,a reliable normal searching range for each current normal vector defined on the initial disparity map is calculated to refine the PatchMatch.Finally,an effective quantizing acceleration strategy is designed to decrease the matching computational cost of continuous disparity.Experimental results demonstrate that the disparity estimation based on our algorithm is better in binocular image benchmarks such as Middlebury and KITTI.We also provide the disparity estimation results for multi-view stereo in real scenes.

关 键 词：stereo matching multiple views disparity estimation hybrid tree PatchMatch 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]