特征融合的双目立体匹配算法加速研究与实现  

Research and Implementation Accelerating of Binocular Stereo Matching Algorithm Based on Feature Fusion

作　　者：范亚博 王国祥[2] 陈海军[2] 冯威 FAN Ya-bo;WANG Guo-xiang;CHEN Hai-jun;FENG Wei(Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University,Chengdu 611756,China;China Railway Eryuan Engineering Group Co.,Ltd.,Chengdu 610031,China)

机构地区：[1]西南交通大学地球科学与环境工程学院,成都611756 [2]中铁二院工程集团有限责任公司,成都610031

出　　处：《导航定位与授时》2022年第6期133-140,共8页Navigation Positioning and Timing

基　　金：国家自然科学基金(42171429);四川省科技计划资助项目(2020GZYZF0010)。

摘　　要：随着图像分辨率和场景信息获取实时性需求的提高,业界对双目立体匹配算法的效率提出了更高的要求。针对该问题,提出了将SAD与Census变换特征融合的结果作为初始匹配代价,利用SGM算法进行代价聚合,采用赢家通吃策略计算视差,通过左右一致性检验检测出遮挡点并填充,使用中值滤波剔除异常值,最终获取优化后的视差图。采用统一计算设备架构(CUDA)对算法实现并行计算,针对立体匹配比较耗时的问题,该算法最大化地利用共享内存、寄存器内存以及CUDA流,实现了不同核函数之间的并行,大大提升了执行效率。结果表明,该算法在Middlebury立体匹配平台上,平均误匹配率下降了8.05%;在NVIDIA GeForce GTX 1650平台上运行450×375分辨率的图像,比原始SGM算法快687倍,运行高分辨率图像时依然能够实现实时显示性能。With the increase in image resolution and the demand for real-time scene information acquisition, the industry has put forward higher demands on the efficiency of binocular stereo matching algorithms. To address this problem, the outcome of the fusion of SAD features and Census transform is proposed as the initial matching cost, cost aggregation is performed using SGM algorithm, parallax is calculated using a winner-take-all strategy, occlusion points are detected and filled by left-right consistency check, outliers are removed using median filtering, and finally the optimized parallax map is obtained. The CUDA is used to parallelize the algorithm. For the problem that stereo matching is relatively time-consuming, the algorithm maximizes the use of shared memory, register memory and the use of CUDA streams to achieve parallelism among different core functions, which greatly improves the execution efficiency. The results show that the algorithm reduces the average false match by 8.05% on the Middlebury stereo matching platform;it is 687 times faster than the original SGM algorithm when running 450×375 resolution images on the NVIDIA GeForce GTX 1650 platform, and it still achieves real-time display performance when running high-resolution images.

关 键 词：立体匹配 SAD Census变换 CUDA加速 并行计算 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]