Lite Any Stereo: Efficient Zero-Shot Stereo Matching

• URL: http://arxiv.org/abs/2511.16555v1
• Date: Thu, 20 Nov 2025 17:07:06 GMT
• Title: Lite Any Stereo: Efficient Zero-Shot Stereo Matching
• Authors: Junpeng Jing, Weixun Luo, Ye Mao, Krystian Mikolajczyk,
• Abstract summary: Lite Any Stereo is a framework that achieves strong zero-shot generalization while remaining highly efficient.<n>Our model attains accuracy comparable to or exceeding state-of-the-art non-prior-based accurate methods.
• Score: 21.89511226115265
• License: http://creativecommons.org/licenses/by/4.0/
• 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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