解决跨算法特征匹配难题，提升不同设备间的视觉定位精度。
MatChA: Cross-Algorithm Matching with Feature Augmentation
- 通过特征增强实现跨检测器的特征匹配
- 在跨算法场景下显著提升图像匹配准确率
- 适合多设备、异构传感器的视觉定位应用
当前先进方法无法处理不同设备使用不同稀疏特征提取算法获取关键点及其描述子的视觉定位场景。仅进行描述子转换不足以应对跨特征检测器的情况，因现有方案假设关键点一致，而实际中常使用不同检测器。关键点重复性低，且描述子缺乏区分性与独特性，导致真实对应关系难以识别。本文提出首个针对此问题的方法，通过特征描述子增强实现跨检测器匹配，并将特征映射至潜在空间。实验表明，该方法显著提升跨特征场景下的图像匹配与视觉定位性能，在多个基准测试上验证了有效性。
原文摘要 · Abstract (English)
State-of-the-art methods fail to solve visual localization in scenarios where different devices use different sparse feature extraction algorithms to obtain keypoints and their corresponding descriptors. Translating feature descriptors is enough to enable matching. However, performance is drastically reduced in cross-feature detector cases, because current solutions assume common keypoints. This means that the same detector has to be used, which is rarely the case in practice when different descriptors are used. The low repeatability of keypoints, in addition to non-discriminatory and non-distinctive descriptors, make the identification of true correspondences extremely challenging. We present the first method tackling this problem, which performs feature descriptor augmentation targeting cross-detector feature matching, and then feature translation to a latent space. We show that our method significantly improves image matching and visual localization in the cross-feature scenario and evaluate the proposed method on several benchmarks.
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