MatChA: Cross-Algorithm Matching with Feature Augmentation
This addresses a practical issue in visual localization for applications using varied devices, but it is incremental as it builds on existing feature translation methods.
The paper tackles the problem of visual localization when different devices use different sparse feature extraction algorithms, which drastically reduces performance due to low keypoint repeatability and non-discriminatory descriptors. The proposed method, MatChA, improves image matching and visual localization in cross-feature scenarios, as evaluated on several benchmarks.
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.