Repeatability Is Not Enough: Learning Affine Regions via Discriminability
A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably matched and this necessitates descriptor-based learning. We explore factors that influence such learning and registration: the loss function, descriptor type, geometric parametrization and the trade-off between matchability and geometric accuracy and propose a novel hard negative-constant loss function for learning of affine regions. The affine shape estimator -- AffNet -- trained with the hard negative-constant loss outperforms the state-of-the-art in bag-of-words image retrieval and wide baseline stereo. The proposed training process does not require precisely geometrically aligned patches.The source codes and trained weights are available at https://github.com/ducha-aiki/affnet
PDF Abstract ECCV 2018 PDF ECCV 2018 AbstractCode
Datasets
HPatches
Oxford5k
IMC PhotoTourism
Results from the Paper
Ranked #4 on
Image Matching
on IMC PhotoTourism
(using extra training data)
