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Lite Any Stereo V2: Faster, Stronger Zero-Shot Stereo Matching

By PulseAugur Editorial · [4 sources] ·

Researchers have introduced Lite Any Stereo V2 (LAS2), a new series of ultra-fast models for efficient zero-shot stereo matching. LAS2 challenges the notion that faster models are less capable by employing a 2D-only cost aggregation framework optimized for real-world latency and a three-stage training strategy. This approach enables smoother synthetic-to-real transfer and improved reliability. LAS2 models demonstrate state-of-the-art accuracy among efficient stereo methods while achieving significantly lower inference times, with LAS2-H outperforming Fast-FoundationStereo by 1.8x and 2.7x on H200 and Orin hardware, respectively. AI

IMPACT This research could enable more efficient deployment of stereo matching capabilities on resource-constrained devices.

RANK_REASON The cluster contains a research paper detailing a new model architecture and training strategy for computer vision.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Lite Any Stereo V2: Faster, Stronger Zero-Shot Stereo Matching

COVERAGE [4]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Lite Any Stereo V2 (LAS2) presents an efficient stereo matching approach that achieves state-of-the-art accuracy with significantly reduced latency through optimized architecture and training strategies.

  2. arXiv cs.CV TIER_1 English(EN) · Mahmoud Tahmasebi, Saif Huq, Kevin Meehan, Marion McAfee ·

    ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matching

    arXiv:2506.21091v2 Announce Type: replace Abstract: Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo matching models that deliver high accuracy while operating in real-time continues to be a major c…

  3. arXiv cs.CV TIER_1 English(EN) · Junpeng Jing, Ronglai Zuo, Zhelun Shen, Shangchen Zhou, Rolandos Alexandros Potamias, Stefanos Zafeiriou, Krystian Mikolajczyk, Jiankang Deng ·

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

    arXiv:2606.24457v1 Announce Type: new Abstract: 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 co…

  4. arXiv cs.CV TIER_1 English(EN) · Jiankang Deng ·

    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 inf…

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