Recent works attempt to extend Graph Convolution Networks (GCNs) to point
clouds for classification and segmentation tasks. These works tend to sample
and group points to create smaller point sets locally and mainly focus on
extracting local features through GCNs, while ignoring the relationship between
point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network
(DHGCN) for explicitly learning the contextual relationships between the
voxelized point parts, which are treated as graph nodes. Motivated by the
intuition that the contextual information between point parts lies in the
pairwise adjacent relationship, which can be depicted by the hop distance of
the graph quantitatively, we devise a novel self-supervised part-level hop
distance reconstruction task and design a novel loss function accordingly to
facilitate training. In addition, we propose the Hop Graph Attention (HGA),
which takes the learned hop distance as input for producing attention weights
to allow edge features to contribute distinctively in aggregation. Eventually,
the proposed DHGCN is a plug-and-play module that is compatible with
point-based backbone networks. Comprehensive experiments on different backbones
and tasks demonstrate that our self-supervised method achieves state-of-the-art
performance. Our source code is available at: this https URL

我们提出了一种名为 DHGCN（Dynamic Hop Graph Convolution Network）的方法，通过学习点部件之间的上下文关系来提取点云中的局部特征，并利用学习到的跳跃距离作为输入进行图注意聚合，通过自我监督方法实现了最先进的性能。