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[论文解读]D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
时间 2020-12-23
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深度学习
pytorch
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文章链接:https://www.jianshu.com/p/b33c7c9ea2e7 以前好久的论文,这几天整理资料也翻出来了。应该是CVPR19的论文,讲的是同时做出来det和des。特征点定位精度不高,速度也很慢,不过对光照等鲁棒性非常高,如下图所示。 但是这个方法的弱点非常明显,定位精度不高,如下图红色圈出的地方,其实非常低的精度了。 实际上用这个进行重建的实验结果表示还不如SIFT好。
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相关文章
1.
[论 文笔记]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.
Bags of Local Convolutional Features for Scalable Instance Search 论文解读
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6.
ASLFeat: Learning Local Features of Accurate Shape and Localization &&2020
7.
1604.Joint Detection and Identification Feature Learning for Person Search论文阅读笔记
8.
[论文解读]ASLFeat: Learning Local Features of Accurate Shape and Localization
9.
[论文阅读] A novel binary shape context for 3D local surface description
10.
Richer Convolutional Features for Edge Detection 论文阅读
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