[論 文筆記]D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
文章目錄 思路 新的數據集 效果 復現 同時進行檢測和描述 檢測關鍵點的方法 各種不變性 訓練 訓練數據的處理 網絡結構 評價準則 關鍵點的檢測關鍵是保證在光照和視點變化下的可重複性，描述子只要是可區分度，主要是用來做匹配的。 思路 這篇文章借用了傳統的特徵點檢測的思路。 之前的大多數網絡，比如lift，都是先進行關鍵點的檢測，然後確定了關鍵點的位置以後再進一步生成描述子，也就是detect-th
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相關文章
- 1. [論文解讀]D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
- 2. [CVPR 2020] D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
- 3. 【點雲識別】D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features(CVPR 2020 Oral)
- 4. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 5. 論文筆記之--Joint Detection and Identification Feature Learning for Person Search
- 6. 論文筆記：Learning Region Features for Object Detection
- 7. ASLFeat: Learning Local Features of Accurate Shape and Localization &&2020
- 8. Richer Convolutional Features for Edge Detection論文筆記
- 9. 論文筆記1——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 10. (2015)Graph-based Anomaly Detection and Description : A survey 論文筆記