PromptStereo: 通过结构和运动提示实现零样本立体匹配
PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts
AI总结：
PromptStereo通过结构和运动提示提升零样本立体匹配的泛化性能，提出PRU模块增强单目深度模型的潜在表示。
摘要：
现代立体匹配方法已经利用单目深度基础模型来实现优越的零样本泛化性能。然而，大多数现有方法主要集中在提取稳健的特征用于成本体积构建或视差初始化。同时，迭代细化阶段，也是零样本泛化至关重要的部分，仍然未被充分探索。一些方法将单目深度先验作为迭代的指导，但传统基于GRU的架构由于表示能力有限而难以利用它们。在本文中，我们提出了Prompt Recurrent Unit（PRU），一种基于单目深度基础模型解码器的新型迭代细化模块。通过将单目结构和立体运动提示整合到解码器中，PRU在丰富单目深度基础模型的潜在表示时保留了其固有的单目深度先验。实验表明，我们的PromptStereo在多个数据集上实现了最先进的零样本泛化性能，同时保持了可比或更快的推理速度。我们的发现突显了提示引导的迭代细化作为零样本立体匹配的有前途的方向。
英文摘要：
Modern stereo matching methods have leveraged monocular depth foundation models to achieve superior zero-shot generalization performance. However, most existing methods primarily focus on extracting robust features for cost volume construction or disparity initialization. At the same time, the iterative refinement stage, which is also crucial for zero-shot generalization, remains underexplored. Some methods treat monocular depth priors as guidance for iteration, but conventional GRU-based architectures struggle to exploit them due to the limited representation capacity. In this paper, we propose Prompt Recurrent Unit (PRU), a novel iterative refinement module based on the decoder of monocular depth foundation models. By integrating monocular structure and stereo motion cues as prompts into the decoder, PRU enriches the latent representations of monocular depth foundation models with absolute stereo-scale information while preserving their inherent monocular depth priors. Experiments demonstrate that our PromptStereo achieves state-of-the-art zero-shot generalization performance across multiple datasets, while maintaining comparable or faster inference speed. Our findings highlight prompt-guided iterative refinement as a promising direction for zero-shot stereo matching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
