融合多模型提升图像匹配精度，应对光照季节变化挑战
Silver medal Solution for Image Matching Challenge 2024
- 采用EfficientNet-B7+余弦距离筛选图像对，结合KeyNet与SuperPoint提取关键点
- 在私有榜单获0.167高分，关键点匹配在纹理与环境变化下表现更优
- 适合需要鲁棒图像匹配的3D建图项目，尤其面对复杂场景变化
Image Matching Challenge 2024旨在从多样图像集构建3D地图，需解决跨视角、光照及季节变化下的图像匹配难题。本项目提出一种融合多种先进方法的流水线：使用预训练EfficientNet-B7进行初始特征提取，并通过余弦距离筛选图像对；采用KeyNetAffNetHardNet与SuperPoint提取关键点特征；利用AdaLAM与SuperGlue完成关键点匹配；最终通过Pycolmap实现3D空间分析。该方法在私有榜单取得0.167的优异成绩，实验表明KeyNetAffNetHardNet与SuperPoint组合在表面纹理与环境条件变化时，显著优于传统算法，有效提升匹配鲁棒性。
原文摘要 · Abstract (English)
Image Matching Challenge 2024 is a competition focused on building 3D maps from diverse image sets, requiring participants to solve fundamental computer vision challenges in image matching across varying angles, lighting, and seasonal changes. This project develops a Pipeline method that combines multiple advanced techniques: using pre-trained EfficientNet-B7 for initial feature extraction and cosine distance-based image pair filtering, employing both KeyNetAffNetHardNet and SuperPoint for keypoint feature extraction, utilizing AdaLAM and SuperGlue for keypoint matching, and finally applying Pycolmap for 3D spatial analysis. The methodology achieved an excellent score of 0.167 on the private leaderboard, with experimental results demonstrating that the combination of KeyNetAffNetHardNet and SuperPoint provides significant advantages in keypoint detection and matching, particularly when dealing with challenging variations in surface texture and environmental conditions that typically degrade traditional algorithm performance.
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