Non-local Low-rank Point Cloud Denoising for 3D Measurement Surfaces

Dingkun ZHU, Honghua CHEN, Weiming WANG, Haoran XIE, Gary CHENG, Mingqiang WEI, Jun WANG, Fu Lee WANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Three-dimensional (3D) imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3D point clouds is, however, usually noisy and should be denoised to facilitate subsequent applications. Existing point cloud denoising methods typically perform 1) point position updating directly or 2) point normal filtering followed by point position updating, and seldom consider the correlation between position updating and normal filtering, leading to less desirable denoised results. This paper proposes a non-local low-rank point cloud denoising framework (NL-PCD) to handle 3D measurement surfaces with different-scale and -type noise. We first design a rotation-invariant feature descriptor, called height and normal patch (HNP), to encode the position and normal information of each point, and search non-local yet geometrically similar HNPs in the whole point cloud. Similar HNPs are then grouped and packed into a noisy matrix which exhibits high rank due to the existence of the noise. Finally, we remove the noise from the noisy matrix through low-rank matrix recovery by making use of non-local similarities among HNPs. In such a way, we can optimize both point positions and normals (i.e., dual geometry domains) in a joint framework to fully exploit the correlation between the two domains for point cloud denoising. Experimental results on synthetic and real-world data demonstrate that our NL-PCD outperforms both traditional and deep learning-based denoising methods in terms of noise removal and feature preservation.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Instrumentation and Measurement
Volume71
Early online date3 Jan 2022
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
This work was supported in part by the Katie Shu Sui Pui Charitable Trust-Academic Publication Fellowship under Grant KSPF2019-02, in part by the Hong Kong Metropolitan University Research under Grant 2020/1.12, in part by the National Natural Science Foundation of China under Grant 61802072, in part by the Direct Grant and the Faculty Research Grant of Lingnan University, Hong Kong, under Grant DR22A2 and Grant DB22A5, and in part by the Free Exploration of Basic Research Project, Local Science and Technology Development Fund Guided by the Central Government of China under Grant 2021Szvup060.

Publisher Copyright:
© 1963-2012 IEEE.

Keywords

  • 3D measurement surfaces
  • Correlation
  • Dual geometry domains
  • Feature preservation
  • Geometry
  • Low-rank matrix recovery
  • Noise measurement
  • Noise reduction
  • Non-local similarity
  • Point cloud compression
  • Point cloud denoising
  • Surface reconstruction
  • Three-dimensional displays
  • low-rank matrix recovery
  • feature preservation
  • point cloud denoising
  • nonlocal similarity
  • 3-D measurement surfaces
  • dual geometry domains

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