Although convolution neural network based stereo matching architectures have
made impressive achievements, there are still some limitations: 1)
Convolutional Feature (CF) tends to capture appearance information, which is
inadequate for accurate matching. 2) Due to the static filters, current
convolution based disparity refinement modules often produce over-smooth
results. In this paper, we present two schemes to address these issues, where
some traditional wisdoms are integrated. Firstly, we introduce a pairwise
feature for deep stereo matching networks, named LSP (Local Similarity
Pattern). Through explicitly revealing the neighbor relationships, LSP contains
rich structural information, which can be leveraged to aid CF for more
discriminative feature description. Secondly, we design a dynamic
self-reassembling refinement strategy and apply it to the cost distribution and
the disparity map respectively. The former could be equipped with the unimodal
distribution constraint to alleviate the over-smoothing problem, and the latter
is more practical. The effectiveness of the proposed methods is demonstrated
via incorporating them into two well-known basic architectures, GwcNet and
GANet-deep. Experimental results on the SceneFlow and KITTI benchmarks show
that our modules significantly improve the performance of the model.

本文提出了两种方案来解决卷积神经网络立体匹配的问题。第一种方案是使用局部相似度模式 (LSP) 的成对特征作为卷积特征 (CF) 的补充，以更具区分性的特征描述。第二种方案是设计动态自组装细化策略，用于成本分布和视差图的细化，从而显著提高了建模的性能。