Computation of stereoscopic depth and disparity map extraction are dynamic research topics. A large variety of algorithms has been developed, among which we cite feature matching, moment extraction, and image representation using descriptors to determine a disparity map. This paper proposes a new method for stereo matching based on Fourier descriptors. The robustness of these descriptors under photometric and geometric transformations provides a better representation of a template or a local region in the image. In our work, we specifically use generalized Fourier descriptors to compute a robust cost function. Then, a box filter is applied for cost aggregation to enforce a smoothness constraint between neighboring pixels. Optimization and disparity calculation are done using dynamic programming, with a cost based on similarity between generalized Fourier descriptors using Euclidean distance. This local cost function is used to optimize correspondences. Our stereo matching algorithm is evaluated using the Middlebury stereo benchmark; our approach has been implemented on parallel high-performance graphics hardware using CUDA to accelerate our algorithm, giving a real-time implementation.

中文翻译：

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使用傅立叶描述符和动态编程在CUDA上进行实时立体声匹配

立体深度的计算和视差图的提取是动态研究的主题。已经开发了各种各样的算法，其中我们引用了特征匹配，矩提取和使用描述符来确定视差图的图像表示。提出了一种基于傅立叶描述符的立体声匹配新方法。这些描述符在光度和几何变换下的鲁棒性提供了图像中模板或局部区域的更好表示。在我们的工作中，我们专门使用广义傅立叶描述符来计算鲁棒的成本函数。然后，将盒式滤波器应用于成本汇总，以在相邻像素之间实施平滑度约束。优化和差异计算是使用动态编程完成的，使用基于欧几里得距离的广义傅立叶描述子之间的相似性，产生的代价。该局部成本函数用于优化对应关系。我们的立体声匹配算法是使用Middlebury立体声基准进行评估的；我们的方法已经在使用CUDA的并行高性能图形硬件上实现，以加速我们的算法，从而实现了实时实现。