【論文筆記】DGCNN：Dynamic Graph CNN for Learning on Point Clouds
摘要 《Dynamic Graph CNN for Learning on Point Clouds》論文作者基於PointNet和PointNet++做了改進，提出了一個針對點雲（Point Clouds）數據進行分類和分割的網絡——DGCNN。 PointNet獨立處理點雲數據中的每個點來實現排列不變性（Permutation Invariance），忽略了點之間的局部特徵。針對這一問
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相關文章
- 1. Dynamic Graph CNN for Learning on Point Clouds 2019 論文筆記
- 2. Dynamic Graph CNN for Learning on Point Clouds 解析
- 3. DGCNN (Dynamic Graph CNN for Learning on Point Clouds,動態圖卷積網絡)
- 4. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 5. Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features 論文筆記 2020-10-06
- 6. Deep Hough Voting for 3D Object Detection in Point Clouds 論文筆記
- 7. 論文閱讀：Driving on Point Clouds
- 8. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 9. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds && NIPS 2018 論文筆記
- 10. Deep Learning for 3D Point Clouds: A Survey