【點雲系列】Grid-GCN for Fast and Scalable Point Cloud Learning
文章目錄 1. 簡介 2. Motivation 3. 思想 3.1 數據構建 CAGQ 3.2 圖卷積 GCA 4. 算法 4.1 採樣方法 4.1.1 Random Voxel Sampling （RVS） 4.1.2 Coverage-Aware Sampling （CAS） 4.2 近鄰查詢 4.2.1 Cube Query 4.2.2 K-Nearest Neighbors 5. 實驗結
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- 1. 【點雲識別】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 《論文閱讀》Grid-GCN for Fast and Scalable Point Cloud Learning
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相關文章
- 1. 【點雲識別】Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR 2020)
- 2. 《論文閱讀》Grid-GCN for Fast and Scalable Point Cloud Learning
- 3. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 4. 點雲深度學習系列三： SPLATNet: Sparse Lattice Networks for Point Cloud Processing
- 5. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation
- 6. 三維點雲學習（5）2-Deep learning for Point Cloud
- 7. [轉]Amazon DynamoDB – a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applicat
- 8. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文解讀
- 9. Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. 翻譯和理解：Deep Closest Point: Learning Representations for Point Cloud Registration