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Enhancing Stereo Matching Domain Generalization With Adversarial Domain Alignment
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DOI：10.1109/tdsc.2026.3692888.png)
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Abstract
En 中文
Recently, state-of-the-art stereo-matching networks trained on large-scale synthetic data have shown remarkable performance. However, their capacity to extrapolate effectively to unseen real-world data, i.e. different domains, remains a challenge. The major difficulty resides in the unforeseeable domain gap when generalizing from synthetic data to real-world data. In this paper, we introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADASM</monospace>, an approach using adversarial domain alignment, designed to enhance the robustness and generalization of stereo-matching networks. It mainly consists of two modules: an end-to-end robustness optimizer and a domain-invariant feature learner. First, we adapt adversarial training into the stereo-matching task to reduce models’ sensitivity to the perturbation in real-world samples. By introducing worst cases into the training space, we take unseen data into account and achieve robust disparity estimation for the end-to-end model. Then, via simulating the real-world noise with gradient-based perturbation, we construct a fictitious domain, which is taken as a referential distribution of the real-world noisy data, for further domain alignment. Specifically, we propose to utilize Maximum Mean Discrepancy to realize domain regularization between the original domain and the fictitious one. Finally, we fuse all aforementioned objectives and propose a unified, simple but effective loss function that can be adapted to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> stereo-matching networks. The extensive experiments show that our method achieves a superior disparity estimation performance on various real-world benchmarks, including KITTI, Middlebury, and DrivingStereo. More importantly, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ADASM</monospace> obtains competitive or even better performance than the fine-tuning strategy, revealing its fine-tuning-free character.
Keywords:
Stereo matching
domain generalization
synthetic-to-real
adversarial domain alignment
adversarial training
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