Year: 2020
Type: article
Abstract: 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.
Authors: Dongbo Zhang, Xuequan Lu, Hong Qin, Ying He
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: EAGER: Exploring Volumetric Modeling and Design Theory for Virtual Environments, G1713734648, EAGER: Exploring Spline Theory for Shapes of Arbitrary Topology, G2470675881, G2877319249, G3274260598, EAGER: Bridging Geometric Manifold Theory and Higher-Dimensional Data Modeling for Visual Computing, CHS: Small: Novel Data-adaptive Analytics for Manifold Informatics: Theory, Algorithms, and Applications, CHS: Small: Novel Method for Vectorization of Arbitrary Natural Images and Its Applications
OpenAlex ID: W3089927720