  Point cloud filtering is a fundamental problem in geometry modeling and
processing. Despite of significant advancement in recent years, the existing
methods still suffer from two issues: 1) they are either designed without
preserving sharp features or less robust in feature preservation; and 2) they
usually have many parameters and require tedious parameter tuning. In this
paper, we propose a novel deep learning approach that automatically and
robustly filters point clouds by removing noise and preserving their sharp
features. Our point-wise learning architecture consists of an encoder and a
decoder. The encoder directly takes points (a point and its neighbors) as
input, and learns a latent representation vector which goes through the decoder
to relate the ground-truth position with a displacement vector. The trained
neural network can automatically generate a set of clean points from a noisy
input. Extensive experiments show that our approach outperforms the
state-of-the-art deep learning techniques in terms of both visual quality and
quantitative error metrics. The source code and dataset can be found at
https://github.com/dongbo-BUAA-VR/Pointfilter.
