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。