FlowNet3D - Learning Scene Flow in 3D Point Clouds[CVPR2019]
[論文鏈接] | [代碼鏈接] \qquad 本文作者提出了一個端到端估計場景流的網絡結構——FLowNet3D。在FLowNet3D中引入了兩個新的點雲學習層：（1）學習聯繫兩幀點雲的flow embedding層；（2）學習將特徵從一個點集傳播到另一個點集的set upconv層。 \qquad FlowNet3D主要包含以上三個模塊：（1）set conv層學習點雲中點的特徵，並對點雲進行下
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- 1. FlowNet3D論文學習筆記
- 2. Deep Learning for 3D Point Clouds: A Survey
- 3. Point Cloud 2019
- 4. 3D Point Clouds-1
- 5. 【點雲識別】Learning to Segment 3D Point Clouds in 2D Image Space (CVPR 2020)
- 6. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. 3D點雲深度學習」綜述論文--Deep Learning for 3D Point Clouds: A Survey
- 8. Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 10. 【綜述翻譯】Deep Learning for 3D Point Clouds: A Survey
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相關文章
- 1. FlowNet3D論文學習筆記
- 2. Deep Learning for 3D Point Clouds: A Survey
- 3. Point Cloud 2019
- 4. 3D Point Clouds-1
- 5. 【點雲識別】Learning to Segment 3D Point Clouds in 2D Image Space (CVPR 2020)
- 6. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. 3D點雲深度學習」綜述論文--Deep Learning for 3D Point Clouds: A Survey
- 8. Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 10. 【綜述翻譯】Deep Learning for 3D Point Clouds: A Survey