TY - JOUR
T1 - Non-local Low-rank Point Cloud Denoising for 3D Measurement Surfaces
AU - ZHU, Dingkun
AU - CHEN, Honghua
AU - WANG, Weiming
AU - XIE, Haoran
AU - CHENG, Gary
AU - WEI, Mingqiang
AU - WANG, Jun
AU - WANG, Fu Lee
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2022/1
Y1 - 2022/1
N2 - 3-D imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3-D point clouds are, 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 article proposes a nonlocal low-rank point cloud denoising framework (NL-PCD) to handle 3-D 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 nonlocal 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 nonlocal 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.
AB - 3-D imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3-D point clouds are, 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 article proposes a nonlocal low-rank point cloud denoising framework (NL-PCD) to handle 3-D 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 nonlocal 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 nonlocal 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.
KW - 3D measurement surfaces
KW - Correlation
KW - Dual geometry domains
KW - Feature preservation
KW - Geometry
KW - Low-rank matrix recovery
KW - Noise measurement
KW - Noise reduction
KW - Non-local similarity
KW - Point cloud compression
KW - Point cloud denoising
KW - Surface reconstruction
KW - Three-dimensional displays
KW - low-rank matrix recovery
KW - feature preservation
KW - point cloud denoising
KW - nonlocal similarity
KW - 3-D measurement surfaces
KW - dual geometry domains
UR - http://www.scopus.com/inward/record.url?scp=85122593533&partnerID=8YFLogxK
U2 - 10.1109/TIM.2021.3139686
DO - 10.1109/TIM.2021.3139686
M3 - Journal Article (refereed)
SN - 0018-9456
VL - 71
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 5002214
ER -