Lite Any Stereo: Efficient Zero-Shot Stereo Matching

Junpeng Jing; Weixun Luo; Ye Mao; Krystian Mikolajczyk

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arXiv · 2511.16555

Lite Any Stereo: Efficient Zero-Shot Stereo Matching

Junpeng Jing·Weixun Luo·Ye Mao·Krystian Mikolajczyk

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Abstract

Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community has regarded efficient models as incapable of zero-shot ability due to their limited capacity. In this paper, we introduce Lite Any Stereo, a stereo depth estimation framework that achieves strong zero-shot generalization while remaining highly efficient. To this end, we design a compact yet expressive backbone to ensure scalability, along with a carefully crafted hybrid cost aggregation module. We further propose a three-stage training strategy on million-scale data to effectively bridge the sim-to-real gap. Together, these components demonstrate that an ultra-light model can deliver strong generalization, ranking 1st across four widely used real-world benchmarks. Remarkably, our model attains accuracy comparable to or exceeding state-of-the-art non-prior-based accurate methods while requiring less than 1% computational cost, setting a new standard for efficient stereo matching.

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BibTeX RIS

Junpeng Jing, Weixun Luo, Ye Mao, Krystian Mikolajczyk. 2026-04-15. Lite Any Stereo: Efficient Zero-Shot Stereo Matching. https://arxiv.org/abs/2511.16555

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