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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.05949 |
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| _version_ | 1866912695880515584 |
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| author | Li, Chang Peng, Xingtao |
| author_facet | Li, Chang Peng, Xingtao |
| contents | Stereo image matching is a fundamental task in computer vision, photogrammetry and remote sensing, but there is an almost unexplored field, i.e., polygon matching, which faces the following challenges: disparity discontinuity, scale variation, training requirement, and generalization. To address the above-mentioned issues, this paper proposes a novel U(PM)$^2$: low-cost unsupervised polygon matching with pre-trained models by uniting automatically learned and handcrafted features, of which pipeline is as follows: firstly, the detector leverages the pre-trained segment anything model to obtain masks; then, the vectorizer converts the masks to polygons and graphic structure; secondly, the global matcher addresses challenges from global viewpoint changes and scale variation based on bidirectional-pyramid strategy with pre-trained LoFTR; finally, the local matcher further overcomes local disparity discontinuity and topology inconsistency of polygon matching by local-joint geometry and multi-feature matching strategy with Hungarian algorithm. We benchmark our U(PM)$^2$ on the ScanNet and SceneFlow datasets using our proposed new metric, which achieved state-of-the-art accuracy at a competitive speed and satisfactory generalization performance at low cost without any training requirement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05949 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | U(PM)$^2$:Unsupervised polygon matching with pre-trained models for challenging stereo images Li, Chang Peng, Xingtao Computer Vision and Pattern Recognition Stereo image matching is a fundamental task in computer vision, photogrammetry and remote sensing, but there is an almost unexplored field, i.e., polygon matching, which faces the following challenges: disparity discontinuity, scale variation, training requirement, and generalization. To address the above-mentioned issues, this paper proposes a novel U(PM)$^2$: low-cost unsupervised polygon matching with pre-trained models by uniting automatically learned and handcrafted features, of which pipeline is as follows: firstly, the detector leverages the pre-trained segment anything model to obtain masks; then, the vectorizer converts the masks to polygons and graphic structure; secondly, the global matcher addresses challenges from global viewpoint changes and scale variation based on bidirectional-pyramid strategy with pre-trained LoFTR; finally, the local matcher further overcomes local disparity discontinuity and topology inconsistency of polygon matching by local-joint geometry and multi-feature matching strategy with Hungarian algorithm. We benchmark our U(PM)$^2$ on the ScanNet and SceneFlow datasets using our proposed new metric, which achieved state-of-the-art accuracy at a competitive speed and satisfactory generalization performance at low cost without any training requirement. |
| title | U(PM)$^2$:Unsupervised polygon matching with pre-trained models for challenging stereo images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.05949 |