Point clouds data, as one kind of representation of 3D objects, are the most
primitive output obtained by 3D sensors. Unlike 2D images, point clouds are
disordered and unstructured. Hence it is not straightforward to apply
classification techniques such as the convolution neural network to point
clouds analysis directly. To solve this problem, we propose a novel network
structure, named Attention-based Graph Convolution Networks (AGCN), to extract
point clouds features. Taking the learning process as a message propagation
between adjacent points, we introduce an attention mechanism to AGCN for
analyzing the relationships between local features of the points. In addition,
we introduce an additional global graph structure network to compensate for the
relative information of the individual points in the graph structure network.
The proposed network is also extended to an encoder-decoder structure for
segmentation tasks. Experimental results show that the proposed network can
achieve state-of-the-art performance in both classification and segmentation
tasks.

提出了名为 AGCN 的新型网络结构，结合了图卷积网络和注意力机制，可以有效地从无序和非结构化的点云数据中提取特征，经实验证明在分类和分割任务中均达到了当前最先进的性能。

基于注意力机制的图卷积神经网络点云学习

Point Clouds Learning with Attention-based Graph Convolution Networks

We propose a novel scene graph generation model called Graph R-CNN, that is
both effective and efficient at detecting objects and their relations in
images. Our model contains a Relation Proposal Network (RePN) that efficiently
deals with the quadratic number of potential relations between objects in an
image. We also propose an attentional Graph Convolutional Network (aGCN) that
effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and
realistic than existing metrics. We report state-of-the-art performance on
scene graph generation as evaluated using both existing and our proposed
metrics.

提出了一种新颖的场景图生成模型 ——Graph R-CNN，其在检测图像中的对象和它们之间的关系方面既有效又高效。该模型包含一种关系提议网络（RePN），能够有效地处理图像中潜在关系的二次数量。同时，我们提出了一种注意力图卷积网络（aGCN），有效地捕捉对象和关系之间的上下文信息。最后，我们引入了一种比现有指标更全面、更实际的评估方法。我们使用现有指标和我们提出的评估方法评估场景图生成，获得了最先进的性能。