  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.
