SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 論文筆記
Magic Leap, ETH Zurich 提出了特徵點匹配網絡SuperGlue，尋找兩幅圖像局部特徵點之間的對應關係的神經網絡，該網絡使用圖神經網絡預測損失。 搭配super point在室內外重定位任務中達到SOTA的精度。 論文方法： 首先輸入圖像對記爲A，B，各包含了關鍵點和局部描述子，因此是集合形式{(p,d)} p=(x,y,c)是關鍵點，其中c是置信度；d是描述子，本文中是sup
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相關文章
- 1. [論文解讀]SuperGlue: Learning Feature Matching with Graph Neural Networks
- 2. 【論文筆記】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
- 3. 論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
- 4. 筆記《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 5. Few-Shot Learning with Graph Neural Networks筆記
- 6. Few-Shot Learning with Graph Neural Networks
- 7. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 8. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 9. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 論文筆記
- 10. 【論文筆記】Graph Matching Networks for Learning the Similarity of Graph Structured Objects