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.

翻译：本文提出DINO-RotateMatch，这是一种旨在解决从非结构化互联网图像进行大规模三维重建时图像匹配挑战的深度学习框架。该方法将数据集自适应的图像配对策略与旋转感知的关键点提取与匹配相结合。DINO被用于在大规模图像集中检索语义相关的图像对，而基于旋转的数据增强则利用ALIKED和Light Glue捕捉方向依赖的局部特征。在Kaggle 2025图像匹配挑战赛上的实验表明，该方法在平均平均精度（mAA）上取得了持续提升，并获得银奖（在943支队伍中排名第47）。结果证实，将自监督全局描述符与旋转增强的局部匹配相结合，为大规模三维重建提供了一个鲁棒且可扩展的解决方案。