@inproceedings{51c1d5033989437792eea2e9a168f4de,
title = "Weighted multi-projection: 3D point cloud denoising with tangent planes",
abstract = "We present a novel algorithm for 3D point cloud denoising called weighted multi-projection. As a collection of 3D points sampled from surfaces of objects, a 3D point cloud is widely used in robotics, autonomous driving and augmented reality. Due to the physical limitations of 3D sensing devices, 3D point clouds are usually noisy, which influences subsequent computations. Compared to many previous denoising works, instead of directly smoothing the coordinates of 3D points, we use a two-fold smoothing. We first estimate a local tangent plane at each 3D point and then reconstruct each 3D point by weighted averaging of its projections on multiple tangent planes. We validate the empirical performance on the dataset of ShapeNetCore and show that weighted multi-projection outperforms its competitors in all nine classes.",
keywords = "3D point cloud, Graph, Manifold, Surface normal, Tangent plane",
author = "Chaojing Duan and Siheng Chen and Jelena Kovacevic",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/GlobalSIP.2018.8646331",
language = "English (US)",
series = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "725--729",
booktitle = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
}