[论 文笔记]D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
文章目录 思路 新的数据集 效果 复现 同时进行检测和描述 检测关键点的方法 各种不变性 训练 训练数据的处理 网络结构 评价准则 关键点的检测关键是保证在光照和视点变化下的可重复性，描述子只要是可区分度，主要是用来做匹配的。 思路 这篇文章借用了传统的特征点检测的思路。 之前的大多数网络，比如lift，都是先进行关键点的检测，然后确定了关键点的位置以后再进一步生成描述子，也就是detect-th
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相关文章
- 1. [论文解读]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. 【点云识别】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论文阅读笔记
- 5. 论文笔记之--Joint Detection and Identification Feature Learning for Person Search
- 6. 论文笔记：Learning Region Features for Object Detection
- 7. ASLFeat: Learning Local Features of Accurate Shape and Localization &&2020
- 8. Richer Convolutional Features for Edge Detection论文笔记
- 9. 论文笔记1——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 10. (2015)Graph-based Anomaly Detection and Description : A survey 论文笔记