Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

本研究提出了一种基于层级神经网络的深度学习算法，可以递归应用 PointNet 模型在输入点集的嵌套划分上，并通过度量空间距离利用上下文信息进行学习，同时通过自适应的集合学习层将不同规模的特征组合，从而有效地、鲁棒地学习点集特征，达到了显著优于当前最先进方法的效果。

PointNet++：在度量空间中对点集进行深层次分层特征学习