We propose a precise and efficient normal estimation method that can deal
with noise and nonuniform density for unstructured 3D point clouds. Unlike
existing approaches that directly take patches and ignore the local
neighborhood relationships, which make them susceptible to challenging regions
such as sharp edges, we propose to learn graph convolutional feature
representation for normal estimation, which emphasizes more local neighborhood
geometry and effectively encodes intrinsic relationships. Additionally, we
design a novel adaptive module based on the attention mechanism to integrate
point features with their neighboring features, hence further enhancing the
robustness of the proposed normal estimator against point density variations.
To make it more distinguishable, we introduce a multi-scale architecture in the
graph block to learn richer geometric features. Our method outperforms
competitors with the state-of-the-art accuracy on various benchmark datasets,
and is quite robust against noise, outliers, as well as the density variations.

本文提出了一种精确高效的方法，用于处理非结构化三维点云上的噪声与非均匀密度，并通过图卷积特征表示学习局部邻域几何信息进行法向量估计，设计了基于注意力机制的自适应模块，进一步增强了法向估计器对点密度变化的鲁棒性，并引入多尺度结构提取图块来学习更丰富的几何特征，在多个基准数据集上实现了最先进的精度和较强的噪声鲁棒性。