Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy with a convolution kernel for images, we define a point-set kernel as a set of learnable points that jointly respond to a set of neighboring data points according to their geometric affinity measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network is able to robustly capture local information and efficiently achieve better performance on major datasets.

本文提出了两个新的操作，帮助PointNet更高效地利用局部结构以提高3D点云的语义学习效果，并通过实验证明，我们的模型可以捕获局部信息并在主要数据集上表现更好。

利用核相关和图池化挖掘点云局部结构