Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective

Understand

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.