LDGCN: Linked Dynamic Graph CNN: Learning on PointCloud via Linking Hierarchical Features 2019 论文笔记
南方科技大学 代码链接https://github.com/KuangenZhang/ldgcnn 本文是基于DGCNN和PointNet进行改进之后的工作，主要是用于点云特征提取任务，可以很方便的扩展到点云分类和点云分割任务。主要工作就是在DGCNN的基础上加了不同层之间的skip connections，作者称之为"link",使得点云分类精度有了0.7% 的提升，而且由于去掉了点云变换网络，
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相关文章
- 1. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 论文笔记 2020-10-06
- 2. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
- 3. Dynamic Graph CNN for Learning on Point Clouds 2019 论文笔记
- 4. Dynamic Graph CNN for Learning on Point Clouds 解析
- 5. 【论文笔记】DGCNN：Dynamic Graph CNN for Learning on Point Clouds
- 6. 【论文笔记】：Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
- 7. 【论文笔记】Deep Learning on Graphs: A Survey
- 8. [论文笔记]EMNLP2019: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks
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