End2End Multi-View Feature Matching with Differentiable Pose Optimization

• URL: http://arxiv.org/abs/2205.01694v3
• Date: Mon, 11 Sep 2023 10:06:19 GMT
• Title: End2End Multi-View Feature Matching with Differentiable Pose Optimization
• Authors: Barbara Roessle and Matthias Nie{\ss}ner
• Abstract summary: 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. We integrate information from multiple views by spanning the graph across multiple frames to predict the matches all at once.
• Score: 2.311583680973075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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