In this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps, which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.

在这项工作中，我们提出了一种基于学习的方法来去噪和细化视差图。所提出的变分网络自然产生于展开应用于在联合视差、颜色和置信度图像空间中定义的变分能量的近端梯度方法的迭代。我们的方法允许利用彩色图像、置信度图和视差图的联合统计来学习强大的协作正则化器。由于我们方法的可变结构，各个步骤可以很容易地可视化，从而实现方法的可解释性。因此，我们可以对我们的方法如何改进和去噪视差图提供有趣的见解。为此，我们可以可视化和解释学习到的过滤器和激活函数，并证明预测的像素级置信度图的可靠性更高。此外，我们的细化模块的基于优化的结构允许我们计算特征视差图，揭示了我们的细化模块的结构特性。我们方法的效率在公开可用的立体声基准 Middlebury 2014 和 Kitti 2015 上得到了证明。