Point cloud filtering, the main bottleneck of which is removing noise (outliers) while preserving geometric features, is a fundamental problem in 3D field. The two-step schemes involving normal estimation and position update have been shown to produce promising results. Nevertheless, the current normal estimation methods including optimization ones and deep learning ones, often either have limited automation or cannot preserve sharp features. In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features. It is a learning method and thus achieves automatic prediction for normals. For training phase, we first generate patch based samples which are then fed to a classification network to classify feature and non-feature points. We finally train the samples of feature and non-feature points separately, to achieve decent results. Regarding testing, given a noisy point cloud, its normals can be automatically estimated. For further point cloud filtering, we iterate the above normal estimation and a current position update algorithm for a few times. Various experiments demonstrate that our method outperforms state-of-the-art normal estimation methods and point cloud filtering techniques, in terms of both quality and quantity.

3D领域的一个基本问题是点云滤波，其主要瓶颈是在保留几何特征的同时消除噪声（异常值）。涉及正常估计和位置更新的两步方案已显示出可喜的结果。但是，当前的常规估计方法（包括优化方法和深度学习方法）通常要么自动化程度有限，要么无法保留鲜明的特征。在本文中，我们提出了一种新颖的保留特征的法线估计方法，用于保留几何特征的点云过滤。这是一种学习方法，因此可以自动预测法线。在训练阶段，我们首先生成基于补丁的样本，然后将样本送入分类网络以对特征点和非特征点进行分类。最后，我们分别训练特征点和非特征点的样本，以获得不错的结果。关于测试，给定一个嘈杂的点云，可以自动估计其法线。对于进一步的点云过滤，我们将上述正常估计和当前位置更新算法重复几次。各种实验表明，我们的方法在质量和数量上都优于最新的常规估计方法和点云过滤技术。