Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling

• Published in: IEEE transactions on visualization and computer graphics Vol. 27; no. 3; pp. 2015 - 2027
• Main Authors: Zhang, Dongbo, Lu, Xuequan, Qin, Hong, He, Ying
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: autoencoder; Automatic point cloud filtering; Coders; Deep learning; Encoders-Decoders; feature-preserving denoising; Filtering; Filtration; Machine learning; Mathematical models; Modelling; Network architecture; Neural networks; Noise measurement; Parameters; Robustness; Source code; Three dimensional models; Three-dimensional displays; Tuning
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2020.3027069

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 article, 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.