Papers › GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

13 Apr 2019CVPR 2019 6arXiv:1904.06587archive 2025-07-28

Feihu Zhang, Victor Prisacariu, Ruigang Yang, Philip H. S. Torr

In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggregation layer which is a differentiable approximation of the semi-global matching, the second is the local guided aggregation layer which follows a traditional cost filtering strategy to refine thin structures. These two layers can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity. In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.

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feihuzhang/GANet officialmentioned in papermentioned on GitHubpytorch report
HKBU-HPML/FADNet mentioned on GitHubpytorch report
skumailraza/FRSNet-GA mentioned on GitHubpytorch report

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load_data feihuzhang/GANet/predict.py official repository ran · honoured contract MIT (permissive) · 49a628e53f46ed75 · report
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Tasks

Stereo Depth EstimationStereo Matching

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Stereo Depth Estimation Spring GA-Net 1px total 23.225 #4 of 4 Archive leaderboard report

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