Stereo matching is a core task for many computer vision and robotics
applications. Despite their dominance in traditional stereo methods, the
hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy
compared to end-to-end deep models. While deep learning representations have
greatly improved the unary terms of the MRF models, the overall accuracy is
still severely limited by the hand-crafted pairwise terms and message passing.
To address these issues, we propose a neural MRF model, where both potential
functions and message passing are designed using data-driven neural networks.
Our fully data-driven model is built on the foundation of variational inference
theory, to prevent convergence issues and retain stereo MRF's graph inductive
bias. To make the inference tractable and scale well to high-resolution images,
we also propose a Disparity Proposal Network (DPN) to adaptively prune the
search space of disparity. The proposed approach ranks $1^{st}$ on both KITTI
2012 and 2015 leaderboards among all published methods while running faster
than 100 ms. This approach significantly outperforms prior global methods,
e.g., lowering D1 metric by more than 50% on KITTI 2015. In addition, our
method exhibits strong cross-domain generalization and can recover sharp edges.
The codes at this https URL .

基于深度学习的神经 MRF 模型在立体匹配任务中取得了显著的性能提升，通过在潜在函数和信息传递方面应用数据驱动的神经网络，该模型有效地解决了传统立体方法中的建模限制和精度问题，并借助变分推断理论保留了立体 MRF 模型的图感知特征。对于高分辨率图像进行可行的推理，并通过适应性地剪枝视差搜索空间的 Disparity Proposal Network (DPN)，使得该方法在 KITTI 2012 和 2015 榜单上名列第一，且运行时间不超过 100 毫秒。与传统的全局方法相比，本方法的性能显著提升，例如在 KITTI 2015 数据集上将 D1 指标降低了 50%，并且具有强大的跨领域泛化能力和恢复锐利边缘的特点。详细代码请参见此链接。