非参数变换和改进动态规划的立体匹配算法

A stereo matching algorithm based on Census transform and improved dynamic programming

针对传统稠密立体匹配方法在非纹理区、深度不连续处和遮挡处存在错误匹配率过高的问题,提出一种基于非参数变换和改进动态规划相结合的立体匹配算法.采用稀疏非参数变换相关方法计算初始局部匹配代价,并利用行列双向约束动态规划算法对匹配代价进行全局优化,在获取初始视差后分别对原始图像每一像素点进行可信性与纹理性检测,最后利用视差平面拟合结果代替非纹理与非可信区域像素点的原始视差,得到稠密视差图.实验表明,该算法具有较高的鲁棒性与匹配精度,尤其在处理图像的非纹理区、深度不连续处和遮挡处,可获得精确的匹配结果.

A stereo matching algorithm based on Census transform and improved dynamic programming is proposed to the problems of traditional dense stereo matching methods, which have high false matching rate in the textureless areas, depth discontinuities and occlusion. The initial local matching cost is calculated by sparse Census transform correlation, and the raw cost is also optimized by a dynamic programming method by involvoing bidirectional constraints of row and column simultaneously. Meanwhile, the confidence and texture of each pixel are measured for reference image. Finally, the disparities of non-confident or textureless pixels are estimated by fitting parameters of a plane model for the corresponding segment, and the dense disparity map was obtained as well. Experiment results demonstrate that the proposed algorithm achieves high matching accuracy and robustness, especially in the textureless areas, depth discontinuities, and occlusion as well.