Deep Learning on Point Sets for 3D Classification and Segmentation
点云数据是一种重要的几何数据结构， 由于其不规则的格式， 大部分研究都集中在怎样将数据转换成3d立体网格. 但是这样的工作是大量的且会出现问题. 所以作者提出一种新的网络结构来处理这样的情况，利用max_pooling来处理3D非有序点云. Net Architect Classification NetWork 该网络的输入是 n \times 3n×3, 其中n是点云的个数.
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相关文章
- 1. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
- 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
- 4. PointNet Deep Learning on Point Sets for 3D Classification and Segmentation
- 5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文笔记
- 6. 《PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation》点网解析
- 7. 论文阅读：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 8. 2020.09.27读 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. 3D点云网络：PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. PointNet:Deep Learning on Point Sets for 3D Classificatiion and Segmentation