Abstract
Stereo foundation models achieve strong zero-shot generalization but remain computationally prohibitive for real-time applications. Efficient stereo architectures, on the other hand, sacrifice robustness for speed and require costly per-domain fine-tuning. To bridge this gap, we present Fast-FoundationStereo, a family of architectures that achieve, for the first time, strong zero-shot generalization at real-time frame rate. We employ a divide-and-conquer acceleration strategy with three components: (1) knowledge distillation to compress the hybrid backbone into a single efficient student; (2) blockwise neural architecture search for automatically discovering optimal cost filtering designs under latency budgets, reducing search complexity exponentially; and (3) structured pruning for eliminating redundancy in the iterative refinement module. Furthermore, we introduce an automatic pseudo-labeling pipeline used to curate 1.4M in-the-wild stereo pairs to supplement synthetic training data and facilitate knowledge distillation. The resulting model can run over 10x faster than FoundationStereo while closely matching its zero-shot accuracy, thus establishing a new state-of-the-art among real-time methods. Project page: this https URL
Abstract (translated)
立体基础模型能够实现强大的零样本泛化能力，但计算成本过高，难以用于实时应用。而高效的立体架构则为了速度牺牲了鲁棒性，并且需要昂贵的领域特定微调。为弥合这一差距，我们提出了Fast-FoundationStereo，这是一个实现了迄今为止首个在实时帧率下达到强大零样本泛化的架构系列。我们采用了一种“分治”加速策略，包括三个组成部分：（1）知识蒸馏将混合骨干网络压缩成一个单一的高效学生模型；（2）块级神经架构搜索，在延迟预算下自动发现最优成本过滤设计，并大幅减少搜索复杂度；（3）结构化剪枝消除迭代细化模块中的冗余。此外，我们还引入了一个自动伪标签流水线，用于整理140万对野外立体图像以补充合成训练数据并促进知识蒸馏。结果模型比FoundationStereo运行速度快超过10倍，同时其零样本精度与后者紧密匹配，从而在实时方法中建立了新的最先进水平。 项目页面: [请在此处输入实际的链接或URL]
URL
https://arxiv.org/abs/2512.11130
