Dynamic Graph CNN for Learning on Point Clouds 2019 論文筆記
DGCNN，繼Pointnet之後點雲特徵提取的又一代表性工作. Pointnet是先在每個點的特徵上各自進行MLP提取(沒有點與點之間的特徵交流),最後使用MAXpool將所有點的特徵融合在一起,得到一個對總體點雲的1024維的特徵描述子,因此可以將Point net作爲點雲全局特徵的提取器. 相比Pointnet,DGCNN則可以對點雲的局部幾何特徵進行提取和利用.DGCNN採用了圖神經網絡對
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相關文章
- 1. Dynamic Graph CNN for Learning on Point Clouds 解析
- 2. 【論文筆記】DGCNN：Dynamic Graph CNN for Learning on Point Clouds
- 3. DGCNN (Dynamic Graph CNN for Learning on Point Clouds,動態圖卷積網絡)
- 4. LDGCN: Linked Dynamic Graph CNN: Learning on PointCloud via Linking Hierarchical Features 2019 論文筆記
- 5. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 論文筆記 2020-10-06
- 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
- 8. Deep Hough Voting for 3D Object Detection in Point Clouds 論文筆記
- 9. Deep Closest Point: Learning Representations for Point Cloud Registration && 2019論文筆記
- 10. USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 論文筆記