【论文笔记】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
目的是为了定位的时候的解决奇异性，目前有很多解决的方法是使用LSTM网络记录之前的query数据，但是由于LSTM所可表达记录的信息有限，因此本文提出一种gnn的方式。 收到slam和三维重建的启发，使用类似pose graph的方式表示网络，这样即使时间上相对较远的帧也可以提供相互关系 为了表示不同帧直接的相互作用，使用cnn表示feature，gnn表示不同feature之间的相互作用
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相关文章
- 1. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 论文笔记
- 2. Few-Shot Learning with Graph Neural Networks笔记
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- 4. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 论文笔记
- 5. 论文阅读笔记《Few-Shot Learning with Graph Neural Networks》
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- 9. Learning Convolutional Neural Networks for Graphs论文笔记
- 10. Deep Neural Networks for Learning Graph Representations论文笔记