We introduce BAFT, a fast binary and quasi affine invariant local image feature. It combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB. BAFT derives its speed and precision from sampling local image patches in a pattern that depends on the second moment matrix of the same image patch. This approach results in a fast but discriminative descriptor, especially for image pairs with large perspective changes.

Our evaluation on 40 different image pairs shows that BAFT increases the area under the precision/recall curve (AUC) compared to traditional descriptors for the majority of image pairs. In addition we show that this improvement comes with a very low performance penalty compared to the similar ORB descriptor. The BAFT source code is available for download.