點雲深度學習系列五： RSNet：Recurrent Slice Networks for 3D Segmentation on Point Clouds
1. 整體框架 初始n*d的點雲，首先經過輸入特徵提取塊(3卷積層，每層64個1*1濾波器)，輸出n*din大小的特徵Fin；通過x、y、z三個方向的切片池化層將無序點雲轉換爲有序序列；採用雙向RNN處理序列，更新特徵；採用切片上池化層映射回每個點；最後經過輸出特徵提取塊(3個1*1卷積層，輸出維度爲512，256，K)，處理Fsu(切片上池化層的輸出)，輸出每個點的預測 2.局部依賴模塊 Sli
相關文章
- 1. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 2. 3D點雲深度學習」綜述論文--Deep Learning for 3D Point Clouds: A Survey
- 3. 3D Point Clouds-1
- 4. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. 點雲深度學習系列三： SPLATNet: Sparse Lattice Networks for Point Cloud Processing
- 6. 【點雲識別】Multi-Path Region Mining ForWeakly Supervised 3D Semantic Segmentation on Point Clouds
- 7. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 8. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 9. [點雲分割]-Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
- 10. Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications閱讀
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相關文章
- 1. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 2. 3D點雲深度學習」綜述論文--Deep Learning for 3D Point Clouds: A Survey
- 3. 3D Point Clouds-1
- 4. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. 點雲深度學習系列三： SPLATNet: Sparse Lattice Networks for Point Cloud Processing
- 6. 【點雲識別】Multi-Path Region Mining ForWeakly Supervised 3D Semantic Segmentation on Point Clouds
- 7. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 8. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 9. [點雲分割]-Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
- 10. Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications閱讀