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Neighbourhood Consensus Networks
Ignacio Rocco · Mircea Cimpoi · Relja Arandjelović · Akihiko Torii · Tomas Pajdla · Josef Sivic

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #118

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.

Author Information

Ignacio Rocco (Inria)
Mircea Cimpoi (CIIRC, CTU Prague)
Relja Arandjelović (DeepMind)
Akihiko Torii (Tokyo Institute of Technology, Japan)
Tomas Pajdla (Czech Technical University in Prague)
Josef Sivic (Inria and Czech Technical University)

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