**Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching**

**Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching**

> Recent advances in stereo matching have achieved remarkable accuracy, but often rely on large models, heavy computation, or additional foundation-model priors, making them difficult to deploy on resource-constrained platforms. In contrast, efficient stereo models offer faster inference but are commonly considered less capable of strong zero-shot generalization. In this paper, we challenge this assumption by introduci...

**来源信息**
- **来源**：HuggingFace Daily Papers（社区热门论文）
- **分类**：论文
- **发布时间**：2026-06-25 08:00（北京时间）
- **原文**：[打开原文](https://huggingface.co/papers/2606.24457)