Rethinking Iterative Stereo Matching from Diffusion Bridge Model
  Perspective
Yuguang Shi
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.