Convolution on 3D point clouds that generalized from 2D grid-like domains is
widely researched yet far from perfect. The standard convolution characterises
feature correspondences indistinguishably among 3D points, presenting an
intrinsic limitation of poor distinctive feature learning. In this paper, we
propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels
for points according to their dynamically learned features. Compared with using
a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud
convolutions, effectively and precisely capturing the diverse relations between
points from different semantic parts. Unlike popular attentional weight
schemes, the proposed AdaptConv implements the adaptiveness inside the
convolution operation instead of simply assigning different weights to the
neighboring points. Extensive qualitative and quantitative evaluations show
that our method outperforms state-of-the-art point cloud classification and
segmentation approaches on several benchmark datasets. Our code is available at
this https URL

本文提出了 Adaptive Graph Convolution (AdaptConv) 来生成自适应的核，以动态地学习点云的特征，从而以灵活、精确地捕捉不同语义部分点之间的多样关系的方式，改善了基于点云的卷积操作的适应能力，并且在几个基准数据集上表现优于当前最先进的点云分类和分割方法。

点云分析的自适应图卷积

Adaptive Graph Convolution for Point Cloud Analysis

Attributed graph clustering is challenging as it requires joint modelling of
graph structures and node attributes. Recent progress on graph convolutional
networks has proved that graph convolution is effective in combining structural
and content information, and several recent methods based on it have achieved
promising clustering performance on some real attributed networks. However,
there is limited understanding of how graph convolution affects clustering
performance and how to properly use it to optimize performance for different
graphs. Existing methods essentially use graph convolution of a fixed and low
order that only takes into account neighbours within a few hops of each node,
which underutilizes node relations and ignores the diversity of graphs. In this
paper, we propose an adaptive graph convolution method for attributed graph
clustering that exploits high-order graph convolution to capture global cluster
structure and adaptively selects the appropriate order for different graphs. We
establish the validity of our method by theoretical analysis and extensive
experiments on benchmark datasets. Empirical results show that our method
compares favourably with state-of-the-art methods.

本文提出了一种自适应图卷积方法来处理带属性的图聚类问题，该方法利用高阶图卷积来捕获全局聚类结构，并针对不同的图自适应地选择适当的阶数，而实验结果表明，该方法与现有的最先进方法相媲美。