The unstructured nature of point clouds demands that local aggregation be
adaptive to different local structures. Previous methods meet this by
explicitly embedding spatial relations into each aggregation process. Although
this coupled approach has been shown effective in generating clear semantics,
aggregation can be greatly slowed down due to repeated relation learning and
redundant computation to mix directional and point features. In this work, we
propose to decouple the explicit modelling of spatial relations from local
aggregation. We theoretically prove that basic neighbor pooling operations can
too function without loss of clarity in feature fusion, so long as essential
spatial information has been encoded in point features. As an instantiation of
decoupled local aggregation, we present DeLA, a lightweight point network,
where in each learning stage relative spatial encodings are first formed, and
only pointwise convolutions plus edge max-pooling are used for local
aggregation then. Further, a regularization term is employed to reduce
potential ambiguity through the prediction of relative coordinates.
Conceptually simple though, experimental results on five classic benchmarks
demonstrate that DeLA achieves state-of-the-art performance with reduced or
comparable latency. Specifically, DeLA achieves over 90\% overall accuracy on
ScanObjectNN and 74\% mIoU on S3DIS Area 5. Our code is available at
this https URL .

基于点云的本地聚合方法，将空间关系与特征融合解耦，通过点卷积和边缘最大池化进行聚合，实验结果表明其在性能和延迟上取得了显著进展。

点云学习的分离式局部聚合

Decoupled Local Aggregation for Point Cloud Learning

Modern graph neural networks (GNNs) learn node embeddings through multilayer
local aggregation and achieve great success in applications on assortative
graphs. However, tasks on disassortative graphs usually require non-local
aggregation. In addition, we find that local aggregation is even harmful for
some disassortative graphs. In this work, we propose a simple yet effective
non-local aggregation framework with an efficient attention-guided sorting for
GNNs. Based on it, we develop various non-local GNNs. We perform thorough
experiments to analyze disassortative graph datasets and evaluate our non-local
GNNs. Experimental results demonstrate that our non-local GNNs significantly
outperform previous state-of-the-art methods on seven benchmark datasets of
disassortative graphs, in terms of both model performance and efficiency.

该研究提出了一种以非局部聚合为基础的简单而有效的图神经网络框架，采用高效的注意力引导排序，用于解决不同类型的图数据集，实验结果表明，在效率和模型性能方面，该方法显著优于先前针对不同类型图案的最先进方法。