Papers › Neighbourhood Consensus Networks

Neighbourhood Consensus Networks

24 Oct 2018NeurIPS 2018 12arXiv:1810.10510archive 2025-07-28

Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic

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.

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JiwonCocoder/matching1 mentioned on GitHubpytorchMIT report
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MutualMatching JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/lib/model_train/trash/model_pixelCT_mask_comb_FB_check_and_score.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT (permissive) · 6143bd380b5003fc · report
conv JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/lib/matching_model.py community (archive-listed) ran · our draft was wrong MIT (permissive) · ea448b10b84d2017 · report
deconv JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/lib/matching_model.py community (archive-listed) ran · our draft was wrong MIT (permissive) · dc4fb7628379691e · report
featureL2Norm JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/trash/train_pixelCT_mask_comb_FB_check_and_score1.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · cf9656ffec66503c · report
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conv4d JiwonCocoder/matching1/lib/conv4d.py community (archive-listed) unverified MIT (permissive) · 8407da62949de1ee · report
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plot_image JiwonCocoder/matching1/lib/plot.py community (archive-listed) unverified MIT (permissive) · 632470f16cec485c · report
unNormMap1D_to_NormMap2D JiwonCocoder/-Joint-Learning-of-Feature-Extraction-and-Cost-Aggregation-for-Semantic-Matching/eval_ncnet.py community (archive-listed) unverified MIT (permissive) · d99bb81598e19de0 · report
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Tasks

Semantic correspondenceVisual Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic correspondence PF-PASCAL NC-Net PCK (weak) 78.9 #15 of 15 Archive leaderboard report

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