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- W4229059082 abstract "Erroneous feature matches have severe impact on subsequent camera pose estimation and often require additional, time-costly measures, like RANSAC, for outlier rejection. Our method tackles this challenge by addressing feature matching and pose optimization jointly. To this end, we propose a graph attention network to predict image correspondences along with confidence weights. The resulting matches serve as weighted constraints in a differentiable pose estimation. Training feature matching with gradients from pose optimization naturally learns to down-weight outliers and boosts pose estimation on image pairs compared to SuperGlue by 6.7% on ScanNet. At the same time, it reduces the pose estimation time by over 50% and renders RANSAC iterations unnecessary. Moreover, we integrate information from multiple views by spanning the graph across multiple frames to predict the matches all at once. Multi-view matching combined with end-to-end training improves the pose estimation metrics on Matterport3D by 18.5% compared to SuperGlue." @default.
- W4229059082 created "2022-05-08" @default.
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- W4229059082 date "2022-05-03" @default.
- W4229059082 modified "2023-09-27" @default.
- W4229059082 title "End2End Multi-View Feature Matching with Differentiable Pose Optimization" @default.
- W4229059082 doi "https://doi.org/10.48550/arxiv.2205.01694" @default.
- W4229059082 hasPublicationYear "2022" @default.
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