Zhu Dongchen, Li Jiamao, Wang Xianshun, Peng Jingquan, Shi Wenjun, Zhang Xiaolin
Bio-Vision System Laboratory, State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2018 Apr 3;18(4):1074. doi: 10.3390/s18041074.
Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.
视差计算对于双目传感器测距至关重要。基于边缘的视差估计是稀疏立体匹配研究中的一个重要分支，在视觉导航中发挥着重要作用。本文提出了一种基于语义边缘的鲁棒稀疏立体匹配方法。首先使用一些简单的匹配代价，然后提出一种新颖的自适应动态规划算法来获得最优解。该算法利用立体图像之间的视差或语义一致性约束来自适应地搜索参数，从而提高了方法的鲁棒性。将所提出的方法分别与传统动态规划方法、一些密集立体匹配方法以及先进的基于边缘的方法进行了定量和定性比较。实验表明，我们的方法在上述比较中能提供卓越的性能。
