Papers › DGC-Net: Dense Geometric Correspondence Network

DGC-Net: Dense Geometric Correspondence Network

19 Oct 2018arXiv:1810.08393archive 2025-07-28

Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, Juho Kannala

This paper addresses the challenge of dense pixel correspondence estimation between two images. This problem is closely related to optical flow estimation task where ConvNets (CNNs) have recently achieved significant progress. While optical flow methods produce very accurate results for the small pixel translation and limited appearance variation scenarios, they hardly deal with the strong geometric transformations that we consider in this work. In this paper, we propose a coarse-to-fine CNN-based framework that can leverage the advantages of optical flow approaches and extend them to the case of large transformations providing dense and subpixel accurate estimates. It is trained on synthetic transformations and demonstrates very good performance to unseen, realistic, data. Further, we apply our method to the problem of relative camera pose estimation and demonstrate that the model outperforms existing dense approaches.

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AaltoVision/DGC-Net mentioned on GitHubpytorchNOASSERTION report

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Tasks

Camera Pose EstimationDense Pixel Correspondence EstimationOptical Flow EstimationTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dense Pixel Correspondence Estimation HPatches DGC-Net aff+tps+homo Viewpoint I AEPE 1.55 #2 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches DGC-Net aff+tps+homo Viewpoint II AEPE 5.53 #2 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches DGC-Net aff+tps+homo Viewpoint III AEPE 8.98 #2 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches DGC-Net aff+tps+homo Viewpoint IV AEPE 11.66 #2 of 8 Archive leaderboard report
Dense Pixel Correspondence Estimation HPatches DGC-Net aff+tps+homo Viewpoint V AEPE 16.70 #2 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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