Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective

• URL: http://arxiv.org/abs/2404.09051v1
• Date: Sat, 13 Apr 2024 17:31:11 GMT
• Title: Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective
• Authors: Yuguang Shi,
• Abstract summary: We propose a novel training approach that incorporates diffusion models into the iterative optimization process. Our model ranks first in the Scene Flow dataset, achieving over a 7% improvement compared to competing methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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