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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/2512.03715 |
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| _version_ | 1866912746296049664 |
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| author | Zhang, Kaichen Sheng, Tianxiang Shi, Xuanming |
| author_facet | Zhang, Kaichen Sheng, Tianxiang Shi, Xuanming |
| contents | This paper presents DINO-RotateMatch, a deep-learning framework designed to address the chal lenges of image matching in large-scale 3D reconstruction from unstructured Internet images. The
method integrates a dataset-adaptive image pairing strategy with rotation-aware keypoint extraction and
matching. DINO is employed to retrieve semantically relevant image pairs in large collections, while
rotation-based augmentation captures orientation-dependent local features using ALIKED and Light Glue. Experiments on the Kaggle Image Matching Challenge 2025 demonstrate consistent improve ments in mean Average Accuracy (mAA), achieving a Silver Award (47th of 943 teams). The results
confirm that combining self-supervised global descriptors with rotation-enhanced local matching offers
a robust and scalable solution for large-scale 3D reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DINO-RotateMatch: A Rotation-Aware Deep Framework for Robust Image Matching in Large-Scale 3D Reconstruction Zhang, Kaichen Sheng, Tianxiang Shi, Xuanming Computer Vision and Pattern Recognition This paper presents DINO-RotateMatch, a deep-learning framework designed to address the chal lenges of image matching in large-scale 3D reconstruction from unstructured Internet images. The method integrates a dataset-adaptive image pairing strategy with rotation-aware keypoint extraction and matching. DINO is employed to retrieve semantically relevant image pairs in large collections, while rotation-based augmentation captures orientation-dependent local features using ALIKED and Light Glue. Experiments on the Kaggle Image Matching Challenge 2025 demonstrate consistent improve ments in mean Average Accuracy (mAA), achieving a Silver Award (47th of 943 teams). The results confirm that combining self-supervised global descriptors with rotation-enhanced local matching offers a robust and scalable solution for large-scale 3D reconstruction. |
| title | DINO-RotateMatch: A Rotation-Aware Deep Framework for Robust Image Matching in Large-Scale 3D Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.03715 |