Recently, iteration-based stereo matching has shown great potential. However,
these models optimize the disparity map using RNN variants. The discrete
optimization process poses a challenge of information loss, which restricts the
level of detail that can be expressed in the generated disparity map. In order
to address these issues, we propose a novel training approach that incorporates
diffusion models into the iterative optimization process. We designed a
Time-based Gated Recurrent Unit (T-GRU) to correlate temporal and disparity
outputs. Unlike standard recurrent units, we employ Agent Attention to generate
more expressive features. We also designed an attention-based context network
to capture a large amount of contextual information. Experiments on several
public benchmarks show that we have achieved competitive stereo matching
performance. Our model ranks first in the Scene Flow dataset, achieving over a
7% improvement compared to competing methods, and requires only 8 iterations to
achieve state-of-the-art results.

通过将扩散模型融入迭代优化过程中，我们提出了一种新的训练方法，采用时间驱动的门控循环单元（T-GRU）和基于注意力的上下文网络，用于改进基于迭代的立体匹配，并在多个公共基准上实现了有竞争力的性能。在 Scene Flow 数据集中，我们的模型排名第一，与竞争方法相比，改善了超过 7％，而且只需 8 次迭代即可实现最先进的结果。

从扩散桥模型视角重新思考迭代立体匹配

Rethinking Iterative Stereo Matching from Diffusion Bridge Model  Perspective

We study the effect of adversarial perturbations of images on the estimates
of disparity by deep learning models trained for stereo. We show that
imperceptible additive perturbations can significantly alter the disparity map,
and correspondingly the perceived geometry of the scene. These perturbations
not only affect the specific model they are crafted for, but transfer to models
with different architecture, trained with different loss functions. We show
that, when used for adversarial data augmentation, our perturbations result in
trained models that are more robust, without sacrificing overall accuracy of
the model. This is unlike what has been observed in image classification, where
adding the perturbed images to the training set makes the model less vulnerable
to adversarial perturbations, but to the detriment of overall accuracy. We test
our method using the most recent stereo networks and evaluate their performance
on public benchmark datasets.

研究了对深度学习模型在视差估计时的影响，发现微小的对抗扰动可以显著改变视差图，并对在不同架构、使用不同损失函数训练的模型也产生影响，通过对抗性数据增广来使用扰动使得训练的模型更加健壮且不影响整体准确性。