CVPR2017_PointNet_ Deep Learning on Point Sets for 3D Classification and Segmentation
Author Charles R. Qi* Hao Su* @ Stanford 第一個是PHD, 第二個是Professor, 兩人一直在一起做些3D的研究 還有個PointNet++ Abstract 點雲是一種很重要的幾何數據結構，因爲它不規則的形式，很多研究都是將它轉換成規則的3D體素或者collections,這樣的話就會造成大量不必要的數據也會產生一些相應的問題。 點雲最接近原始傳感數
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相關文章
- 1. Deep Learning on Point Sets for 3D Classification and Segmentation
- 2. CVPR2017_PointNet_ Deep Learning on Point Sets for 3D Classification and Segmentation
- 3. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
- 4. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. PointNet Deep Learning on Point Sets for 3D Classification and Segmentation
- 6. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 論文筆記
- 7. 《PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation》點網解析
- 8. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. 2020.09.27讀 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. 3D點雲網絡：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation