Dynamic Graph CNN for Learning on Point Clouds 解析
該文章是最新出的一篇針對Point Clouds數據分類、分割的網路，它是受點雲數據處理的鼻祖論文PointNet、PointNet++的啓發所進行的修改，PointNet只是獨立處理每個點，來實現permutation invariance，但忽視了點之間的局部特徵。所以這篇文章主要提出了一個EdgeConv層來實現獲取局部特徵，解決PointNet所沒有的處理局部特徵的問題。 一、
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- 1. DGCNN (Dynamic Graph CNN for Learning on Point Clouds,動態圖卷積網絡)
- 2. Dynamic Graph CNN for Learning on Point Clouds 2019 論文筆記
- 3. 【論文筆記】DGCNN：Dynamic Graph CNN for Learning on Point Clouds
- 4. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
- 5. Deep Learning for 3D Point Clouds: A Survey
- 6. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 7. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 論文筆記 2020-10-06
- 8. KPConv: Flexible and Deformable Convolution for Point Clouds
- 9. 3D Point Clouds-1
- 10. LDGCN: Linked Dynamic Graph CNN: Learning on PointCloud via Linking Hierarchical Features 2019 論文筆記
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相關文章
- 1. DGCNN (Dynamic Graph CNN for Learning on Point Clouds,動態圖卷積網絡)
- 2. Dynamic Graph CNN for Learning on Point Clouds 2019 論文筆記
- 3. 【論文筆記】DGCNN：Dynamic Graph CNN for Learning on Point Clouds
- 4. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
- 5. Deep Learning for 3D Point Clouds: A Survey
- 6. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 7. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 論文筆記 2020-10-06
- 8. KPConv: Flexible and Deformable Convolution for Point Clouds
- 9. 3D Point Clouds-1
- 10. LDGCN: Linked Dynamic Graph CNN: Learning on PointCloud via Linking Hierarchical Features 2019 論文筆記