Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
論文鏈接 \qquad 作者提出了一種輕量級的局部依賴建模模塊，利用slice pooling layer將無序的點特徵集轉換爲有序的特徵向量序列。 \qquad 首先按照各個座標軸將點雲且爲等距的塊，把每個塊內點的特徵聚合爲一個塊的特徵，用這個特徵向量表示該塊的全局信息。 \qquad 不同塊的特徵按照按照座標組成了一個有序的特徵向量序列，通過RNN來建模序列中的依賴，這使得序列中的上下文相互影
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相關文章
- 1. 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation[ECCV2018]
- 2. 點雲深度學習系列五： RSNet：Recurrent Slice Networks for 3D Segmentation on Point Clouds
- 3. Paper5：Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Per
- 4. Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 6. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. 3D圖像分割：Thickened 2D Networks for 3D Medical Image Segmentation
- 8. A Critical Review of Recurrent Neural Networks for Sequence Learning
- 9. The Unreasonable Effectiveness of Recurrent Neural Networks
- 10. Taming Recurrent Neural Networks for Better Summarization