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OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline

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arxiv 2312.00343 v8 pith:7MM3HWKM submitted 2023-12-01 cs.CV

Xianda Guo , Chenming Zhang , Juntao Lu , Yiqun Duan , Yiqi Wang , Tian Yang , Zheng Zhu , Long Chen This is my paper · ORCID
classification cs.CV
keywords matchingstereoopenstereocomprehensiveperformancestereobasestrongavailable
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Stereo matching aims to estimate the disparity between matching pixels in a stereo image pair, which is important to robotics, autonomous driving, and other computer vision tasks. Despite the development of numerous impressive methods in recent years, determining the most suitable architecture for practical application remains challenging. Addressing this gap, our paper introduces a comprehensive benchmark focusing on practical applicability rather than solely on individual models for optimized performance. Specifically, we develop a flexible and efficient stereo matching codebase, called OpenStereo. OpenStereo includes training and inference codes of more than 10 network models, making it, to our knowledge, the most complete stereo matching toolbox available. Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper. Additionally, we conduct an exhaustive analysis and deconstruction of recent developments in stereo matching through comprehensive ablative experiments. These investigations inspired the creation of StereoBase, a strong baseline model. Our StereoBase ranks 1st on SceneFlow, KITTI 2015, 2012 (Reflective) among published methods and achieves the best performance across all metrics. In addition, StereoBase has strong cross-dataset generalization. Code is available at \url{https://github.com/XiandaGuo/OpenStereo}.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoStereo: A Unified Stereo Geometry Estimation Framework for Disparity and Surface Normal

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A unified stereo framework couples feed-forward disparity matching with a diffusion-based normal estimator through disparity-to-normal initialization and warped right-view conditioning, claiming zero-shot SOTA on seve...

  2. AquaStereo: Enabling Underwater Stereo Matching via Depth-Conditioned Diffusion and Geometry Self-Distillation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Depth-conditioned diffusion, geometry self-distillation, and perception frames yield strong zero-shot underwater stereo matching without real underwater labels.

  3. Iterative Volume Fusion for Asymmetric Stereo Matching

    cs.CV 2025-08 conditional novelty 6.0 of 10

    IVF-AStereo fuses correlation and concatenation cost volumes in two phases to keep stereo disparity accurate when camera views differ in resolution or color.

  4. Leveraging Sparse LiDAR for RAFT-Stereo: A Depth Pre-Fill Perspective

    cs.CV 2025-07 conditional novelty 6.0 of 10

    GRAFT-Stereo pre-fills sparse LiDAR disparities and fuses LiDAR XYZ cues early to make RAFT-Stereo work well with only a few hundred LiDAR points.

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