Point clouds are an increasingly relevant data type but they are often
corrupted by noise. We propose a deep neural network based on
graph-convolutional layers that can elegantly deal with the
permutation-invariance problem encountered by learning-based point cloud
processing methods. The network is fully-convolutional and can build complex
hierarchies of features by dynamically constructing neighborhood graphs from
similarity among the high-dimensional feature representations of the points.
When coupled with a loss promoting proximity to the ideal surface, the proposed
approach significantly outperforms state-of-the-art methods on a variety of
metrics. In particular, it is able to improve in terms of Chamfer measure and
of quality of the surface normals that can be estimated from the denoised data.
We also show that it is especially robust both at high noise levels and in
presence of structured noise such as the one encountered in real LiDAR scans.

本文提出一种基于图卷积层的深度神经网络，能够优雅地处理学习型点云处理方法所遇到的置换不变性问题，通过动态构建邻域图来建立特征的复杂层次结构，与促进接近理想表面的损失相结合，该方法在各种指标上都明显优于现有方法，特别是在高噪声和结构噪声等实际情况下表现尤为稳健。

学习图卷积表示用于点云去噪

Learning Graph-Convolutional Representations for Point Cloud Denoising

Recovering an image from a noisy observation is a key problem in signal
processing. Recently, it has been shown that data-driven approaches employing
convolutional neural networks can outperform classical model-based techniques,
because they can capture more powerful and discriminative features. However,
since these methods are based on convolutional operations, they are only
capable of exploiting local similarities without taking into account non-local
self-similarities. In this paper we propose a convolutional neural network that
employs graph-convolutional layers in order to exploit both local and non-local
similarities. The graph-convolutional layers dynamically construct
neighborhoods in the feature space to detect latent correlations in the feature
maps produced by the hidden layers. The experimental results show that the
proposed architecture outperforms classical convolutional neural networks for
the denoising task.

本文中，作者提出了一种使用图卷积层的卷积神经网络，以利用局部和非局部相似性以实现去噪任务，并取得了比传统卷积神经网络更好的效果。