Due to the difficulty in obtaining real samples and ground truth, the
generalization performance and the fine-tuned performance are critical for the
feasibility of stereo matching methods in real-world applications. However, the
presence of substantial disparity distributions and density variations across
different datasets presents significant challenges for the generalization and
fine-tuning of the model. In this paper, we propose a novel stereo matching
method, called SR-Stereo, which mitigates the distributional differences across
different datasets by predicting the disparity clips and uses a loss weight
related to the regression target scale to improve the accuracy of the disparity
clips. Moreover, this stepwise regression architecture can be easily extended
to existing iteration-based methods to improve the performance without changing
the structure. In addition, to mitigate the edge blurring of the fine-tuned
model on sparse ground truth, we propose Domain Adaptation Based on Pre-trained
Edges (DAPE). Specifically, we use the predicted disparity and RGB image to
estimate the edge map of the target domain image. The edge map is filtered to
generate edge map background pseudo-labels, which together with the sparse
ground truth disparity on the target domain are used as a supervision to
jointly fine-tune the pre-trained stereo matching model. These proposed methods
are extensively evaluated on SceneFlow, KITTI, Middbury 2014 and ETH3D. The
SR-Stereo achieves competitive disparity estimation performance and
state-of-the-art cross-domain generalisation performance. Meanwhile, the
proposed DAPE significantly improves the disparity estimation performance of
fine-tuned models, especially in the textureless and detail regions.

本文提出了一种名为 SR-Stereo 的新型立体匹配方法，通过预测视差剪辑和使用与回归目标尺度相关的损失权重来缓解不同数据集之间的分布差异，并可轻松扩展到现有的基于迭代的方法以改善性能，同时提出了基于预训练边缘的领域自适应（DAPE）来解决粗糙地面实况模型的边缘模糊问题，并在多个数据集上进行了广泛评估，实验结果表明 SR-Stereo 在视差估计性能和跨领域推广性能方面均具有竞争力，DAPE 显著改善了特别是在无纹理和细节区域的精调模型的视差估计性能。

稳健的立体匹配的逐步回归和预训练边缘

Stepwise Regression and Pre-trained Edge for Robust Stereo Matching

Stereo matching methods based on iterative optimization, like RAFT-Stereo and
IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching.
However, these methods struggle to simultaneously capture high-frequency
information in edges and low-frequency information in smooth regions due to the
fixed receptive field. As a result, they tend to lose details, blur edges, and
produce false matches in textureless areas. In this paper, we propose Selective
Recurrent Unit (SRU), a novel iterative update operator for stereo matching.
The SRU module can adaptively fuse hidden disparity information at multiple
frequencies for edge and smooth regions. To perform adaptive fusion, we
introduce a new Contextual Spatial Attention (CSA) module to generate attention
maps as fusion weights. The SRU empowers the network to aggregate hidden
disparity information across multiple frequencies, mitigating the risk of vital
hidden disparity information loss during iterative processes. To verify SRU's
universality, we apply it to representative iterative stereo matching methods,
collectively referred to as Selective-Stereo. Our Selective-Stereo ranks
$1^{st}$ on KITTI 2012, KITTI 2015, ETH3D, and Middlebury leaderboards among
all published methods. Code is available at
this https URL

基于选择性循环单元（SRU）和上下文空间注意力（CSA）模块的选择性立体匹配方法能够同时捕捉边缘的高频信息和平滑区域的低频信息，以减少细节丢失、边缘模糊和纹理区域的错误匹配。