Existing point cloud learning methods aggregate features from neighbouring
points relying on constructing graph in the spatial domain, which results in
feature update for each point based on spatially-fixed neighbours throughout
layers. In this paper, we propose a dynamic feature aggregation (DFA) method
that can transfer information by constructing local graphs in the feature
domain without spatial constraints. By finding k-nearest neighbors in the
feature domain, we perform relative position encoding and semantic feature
encoding to explore latent position and feature similarity information,
respectively, so that rich local features can be learned. At the same time, we
also learn low-dimensional global features from the original point cloud for
enhancing feature representation. Between DFA layers, we dynamically update the
constructed local graph structure, so that we can learn richer information,
which greatly improves adaptability and efficiency. We demonstrate the
superiority of our method by conducting extensive experiments on point cloud
classification and segmentation tasks. Implementation code is available:
this https URL

本文提出了一种动态特征聚合 (DFA) 方法，通过在特征域内构建局部图，进行 k - 最近邻探索潜在位置和特征相似性信息，结合原始点云低维全局特征表达，以增强特征表示，从而有效地进行点云分类。

点云学习的动态局部特征聚合

Dynamic Local Feature Aggregation for Learning on Point Clouds

Deep convolutional neural network (CNN) based models are vulnerable to the
adversarial attacks. One of the possible reasons is that the embedding space of
CNN based model is sparse, resulting in a large space for the generation of
adversarial samples. In this study, we propose a method, denoted as Dynamic
Feature Aggregation, to compress the embedding space with a novel
regularization. Particularly, the convex combination between two samples are
regarded as the pivot for aggregation. In the embedding space, the selected
samples are guided to be similar to the representation of the pivot. On the
other side, to mitigate the trivial solution of such regularization, the last
fully-connected layer of the model is replaced by an orthogonal classifier, in
which the embedding codes for different classes are processed orthogonally and
separately. With the regularization and orthogonal classifier, a more compact
embedding space can be obtained, which accordingly improves the model
robustness against adversarial attacks. An averaging accuracy of 56.91% is
achieved by our method on CIFAR-10 against various attack methods, which
significantly surpasses a solid baseline (Mixup) by a margin of 37.31%. More
surprisingly, empirical results show that, the proposed method can also achieve
the state-of-the-art performance for out-of-distribution (OOD) detection, due
to the learned compact feature space. An F1 score of 0.937 is achieved by the
proposed method, when adopting CIFAR-10 as in-distribution (ID) dataset and
LSUN as OOD dataset. Code is available at
this https URL.

本文提出一种名为动态特征聚合的方法，旨在通过优化正则化和引入正交分类器的方式，压缩卷积神经网络 (CNN) 建模中的嵌入空间和提高模型的鲁棒性，从而更好地应对对抗攻击。在 CIFAR-10 数据集上，我们的方法平均准确率为 56.91％，优于 Mixup 基线 37.31％；此外，我们的方法还在超出分布检测方面实现了最佳性能。