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深度学习3D网络---PointNet++ - 半夜打老虎 - 博客园 PointNet++网络结构如图所示，主要包含set abstraction（SA）块，分割网络中上采样的插值操作 (interpolate)，其中SA由 sampling layer grouping layer和pointnet layer 构成，接下来依次对其进行介绍。 考虑到点云数量通常较大且数量不一致，PointNet++采用最远点采样（FPS (farthest point sampling )）从原始 N N 个点云中获取 N ′ N ′ 个样本进行特征提取。
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a . . . PointNet++ is a follow-up project that builds on and extends PointNet It is version 2 0 of the PointNet architecture PointNet (the v1 model) either transforms features of individual points independently or process global features of the entire point set
PointNet++ - Stanford University PointNet++ Architecture for Point Set Segmentation and Classification We introduce a type of novel neural network, named as PointNet++, to process a set of points sampled in a metric space in a hierarchical fashion (2D points in Euclidean space are used for this illustration)
【综述】PointNet、PointNet++、 F-PointNet基于深度学习 . . . PointNet 是由斯坦福大学的Charles R Qi等人在《PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation》一文中提出的模型，它可以直接对点云进行处理的，对输入点云中的每一个点，学习其对应的空间编码，之后再利用所有点的特征得到一个全局的点云
PointNet++论文解读以及代码分析（超全） - 知乎 文章核心的一点就是提出了多层次特征提取结构。 具体而言就是在输入点集中利用 farthest point sampling 选择一些点作为中心点，然后围绕每个中心点选择周围的点组成一个区域，将每个区域作为PointNet的一个输入样本，这样就得到了一组该区域的特征。