PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space论文解读
PointNet++ 核心问题：点云的分类/语义分割 1、网络构成 PointNet提取特征的方式是对所有点云数据提取了一个全局的特征（见上图），显然，这和目前流行的CNN逐层提取局部特征的方式不一样。受到CNN的启发，作者提出了PointNet++，它能够在不同尺度提取局部特征，通过多层网络结构得到深层特征。PointNet++由以下几个关键部分构成： 1.1采样层（sampling） 激光
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相关文章
- 1. 2020.10.20读 PointNet++： Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- 2. 《PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space》剖析笔记
- 3. 论文阅读：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 4. [Point Cloud] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文解读
- 5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 论文笔记
- 6. 2020.09.27读 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 7. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
- 8. [论文解读+复现] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 9. 【论文阅读笔记】PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 10. PointNet Deep Learning on Point Sets for 3D Classification and Segmentation