In IEEE transactions on visualization and computer graphics
Point cloud filtering is a fundamental problem in geometry modeling and processing. Despite of advancement in recent years, the existing methods still suffer from two issues: 1) they are either designed without preserving sharp features or or less robust in features 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 is going 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. We will make our code and dataset publicly available.
Zhang Dongbo, Lu Xuequan, Qin Hong, He Ying