A Comprehensive Survey on Stereo Matching and Depth Estimation: From Classical Pipelines to Deep Learning Architectures
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| Format: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902271191678976 |
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| author | Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard |
| author_facet | Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard |
| contents | <div>Stereo matching and depth estimation are funda</div> <div>mental components for 3D scene understanding in applications</div> <div>ranging from autonomous driving and robotics to augmented</div> <div>reality and medical imaging. This comprehensive survey unifies</div> <div>classical stereo pipelines with modern deep learning architec</div> <div>tures, providing systematic analysis of cost volume construction,</div> <div>aggregation strategies, and optimization techniques. We present</div> <div>verifiable benchmark comparisons across major datasets (KITTI,</div> <div>Middlebury, ETH3D, Scene Flow), analyze the evolution from</div> <div>hand-crafted features to learned representations, and examine</div> <div>training paradigms including supervised, self-supervised, and</div> <div>cross-domain approaches. Our analysis covers architectural</div> <div>innovations including 3D CNNs, recurrent refinement networks,</div> <div>transformer-based methods, and hybrid approaches. We provide</div> <div>practical guidelines for reproducible evaluation, discuss deploy</div> <div>ment considerations for real-world applications, and identify key</div> <div>challenges including occlusion handling, domain adaptation, and</div> <div>computational efficiency. This survey serves as both a historical</div> <div>perspective on the field’s evolution and a practical guide for</div> <div>researchers and practitioners working on stereo vision systems.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17070403 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Comprehensive Survey on Stereo Matching and Depth Estimation: From Classical Pipelines to Deep Learning Architectures Lin, Yida Xue, Bing Zhang, Mengjie Schofield, Sam Green, Richard <div>Stereo matching and depth estimation are funda</div> <div>mental components for 3D scene understanding in applications</div> <div>ranging from autonomous driving and robotics to augmented</div> <div>reality and medical imaging. This comprehensive survey unifies</div> <div>classical stereo pipelines with modern deep learning architec</div> <div>tures, providing systematic analysis of cost volume construction,</div> <div>aggregation strategies, and optimization techniques. We present</div> <div>verifiable benchmark comparisons across major datasets (KITTI,</div> <div>Middlebury, ETH3D, Scene Flow), analyze the evolution from</div> <div>hand-crafted features to learned representations, and examine</div> <div>training paradigms including supervised, self-supervised, and</div> <div>cross-domain approaches. Our analysis covers architectural</div> <div>innovations including 3D CNNs, recurrent refinement networks,</div> <div>transformer-based methods, and hybrid approaches. We provide</div> <div>practical guidelines for reproducible evaluation, discuss deploy</div> <div>ment considerations for real-world applications, and identify key</div> <div>challenges including occlusion handling, domain adaptation, and</div> <div>computational efficiency. This survey serves as both a historical</div> <div>perspective on the field’s evolution and a practical guide for</div> <div>researchers and practitioners working on stereo vision systems.</div> |
| title | A Comprehensive Survey on Stereo Matching and Depth Estimation: From Classical Pipelines to Deep Learning Architectures |
| url | https://doi.org/10.5281/zenodo.17070403 |