In this work, we propose a learning-based method to denoise and refine
disparity maps of a given stereo method. 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. The efficiency of our method is
demonstrated by the publicly available stereo benchmarks Middlebury 2014 and
Kitti 2015.

本文提出了一种基于学习的方法来去噪和优化所给定的立体方法的视差图。该方法通过资源一个由联合视差、颜色和置信度图像空间中的变分能量定义的近端梯度方法的迭代来自然生成。我们的方法允许学习鲁棒的协作正则化器，利用颜色图像、置信度图和视差图的联合统计信息。由于我们的方法是变分结构的，因此可以轻松可视化单个步骤，从而实现可解释性。因此，我们可以提供如何优化和去噪视差图的有趣见解。我们的方法的效率得到了公开可用的 Middlebury 2014 和 Kitti 2015 立体基准测试的证明。