Point clouds are an increasingly relevant geometric data type but they are often corrupted by noise and affected by the presence of outliers. We propose a deep learning method that can simultaneously denoise a point cloud and remove outliers in a single model. The core of the proposed method is a graph-convolutional neural network able to efficiently deal with the irregular domain and the permutation invariance problem typical of point clouds. 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. The proposed approach outperforms state-of-the-art denoising methods showing robust performance in the challenging setup of high noise levels and in presence of structured noise.

中文翻译：

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学习鲁棒图卷积表示进行点云去噪

点云是一种越来越重要的几何数据类型，但是它们经常被噪声破坏并受到异常值的影响。我们提出了一种深度学习方法，该方法可以在单个模型中同时对点云进行降噪并消除离群值。该方法的核心是图卷积神经网络，它能够有效地处理点云典型的不规则域和置换不变性问题。该网络是完全卷积的，可以通过根据点的高维特征表示之间的相似度动态构建邻域图来构建复杂的特征层次。所提出的方法优于最新的降噪方法，该方法在具有挑战性的高噪声水平设置中和存在结构噪声的情况下表现出强大的性能。