Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective
<|reference_start|>Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective: 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.<|reference_end|>
@article{shi2024rethinking,
    title={Rethinking Iterative Stereo Matching from Diffusion Bridge Model
  Perspective},
    author={Yuguang Shi},
    journal={arXiv preprint arXiv:2404.09051},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09051},
    primaryClass={cs.CV cs.AI}
}