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electrical engineering computing, iterative methods,
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Graph signal processing, point cloud denoising, low-dimensional manifold
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3D point cloud, graph signal processing, denoising,
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image denoising, unsupervised learning,
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2009
Convolution, Sensors, Task analysis, Cameras,
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Uncertainty-Aware CNNs for Depth Completion:
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Uncertainty, Task analysis, Probabilistic logic,
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2012
Convolution, Generators, Generative adversarial networks,
Feature extraction, Data models, Generative adversarial networks,
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Point cloud denoising, Color denoising, Convex optimization,
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2107
Noise reduction, Manifolds, Optimization, Minimization, Laser radar,
Surface reconstruction, Dynamic point cloud denoising, temporal consistency
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2109
Point cloud denoising, Adaptive curvature threshold,
Structure-aware descriptor, Projective height vector,
Improved weighted nuclear norm minimization
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RS(13), No. 16, 2021, pp. xx-yy.
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Gao, R.[Rui],
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2202
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2203
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2210
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Zhou, H.R.[Hao-Ran],
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2212
Estimation, Point cloud compression, Noise measurement, Feature extraction,
Task analysis, Surface reconstruction, point cloud denoising
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Wei, Z.Y.[Ze-Yong],
Chen, H.H.[Hong-Hua],
Nan, L.L.[Liang-Liang],
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PathNet: Path-Selective Point Cloud Denoising,
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IEEE DOI
2405
Noise reduction, Point cloud compression, Noise measurement,
Surface reconstruction, Geometry, Reinforcement learning,
reinforcement learning
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2212
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Si, S.M.[Shu-Ming],
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2301
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Elsevier DOI
2303
Point cloud cleaning, Denoising, Outlier removal, Neural networks
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PointFilterNet: A Filtering Network for Point Cloud Denoising,
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IEEE DOI
2303
Point cloud compression, Noise reduction, Noise measurement,
Deep learning, Image reconstruction, Training, HVS
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Hu, X.[Xin],
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A Noising-Denoising Framework for Point Cloud Upsampling via
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Elsevier DOI
2305
Point cloud, Arbitrary ratio upsampling, Normalizing flows
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Li, Y.H.[Yu-Hao],
Zou, X.[Xianghong],
Li, T.[Tian],
Sun, S.[Sihan],
Wang, Y.[Yuan],
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Li, J.P.[Jiang-Ping],
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MuCoGraph: A multi-scale constraint enhanced pose-graph framework for
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Elsevier DOI
2310
Mobile laser scanning point cloud,
Position inconsistency correction, Multi-scale constraint, Graph optimization
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Wang, X.T.[Xing-Tao],
Cui, W.X.[Wen-Xue],
Xiong, R.Q.[Rui-Qin],
Fan, X.P.[Xiao-Peng],
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CirSysVideo(33), No. 11, November 2023, pp. 6288-6301.
IEEE DOI
2311
BibRef
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Wang, J.[Jinli],
Nonparametric point cloud filter,
IET-IPR(18), No. 2, 2024, pp. 388-402.
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2402
adaptive filters, filtering theory
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Plant-Denoising-Net (PDN): A plant point cloud denoising network
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2404
Point cloud denoising, Deep learning, Phenotyping,
Multi-feature fusion, Density gradient field, Gradient ascent
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Wang, L.C.[Lian-Chao],
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DCOR: Dynamic Channel-Wise Outlier Removal to De-Noise LiDAR Data
Corrupted by Snow,
ITS(25), No. 7, July 2024, pp. 7017-7028.
IEEE DOI
2407
Laser radar, Snow, Meteorology, Point cloud compression,
Performance evaluation, Noise reduction, LiDAR data, dynamic search radius
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Zhang, B.C.[Bai-Chuan],
Liu, Y.X.[Yan-Xiong],
Dong, Z.P.[Zhi-Peng],
Li, J.[Jie],
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Tang, Q.[Qiuhua],
Huang, G.[Guoan],
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An Optimal Denoising Method for Spaceborne Photon-Counting LiDAR
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PU-Mask: 3D Point Cloud Upsampling via an Implicit Virtual Mask,
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WWW Link.
2407
Point cloud compression, Feature extraction, Surface treatment,
graph Laplacian
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Cao, Y.K.[Yun-Kang],
Xu, X.H.[Xiao-Hao],
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Complementary pseudo multimodal feature for point cloud anomaly
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PR(156), 2024, pp. 110761.
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2408
Point cloud, Anomaly detection, Pre-trained representation,
Multimodal learning, Rendering
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Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Wang, Y.Q.[Ying-Qian],
Dong, X.Y.[Xiao-Yu],
Xu, Q.Y.[Qing-Yu],
Yang, J.[Jungang],
An, W.[Wei],
Unsupervised Degradation Representation Learning for Unpaired
Restoration of Images and Point Clouds,
PAMI(47), No. 1, January 2025, pp. 1-18.
IEEE DOI
2412
Degradation, Image restoration, Point cloud compression, Training,
Data mining, Representation learning, Neural networks, neural network
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Ambrosino, A.[Antonella],
di Benedetto, A.[Alessandro],
Fiani, M.[Margherita],
Hybrid Denoising Algorithm for Architectural Point Clouds Acquired
with SLAM Systems,
RS(16), No. 23, 2024, pp. 4559.
DOI Link
2501
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Li, W.[Wei],
Dong, Q.H.[Qing-Hai],
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A New Multi-Channel Triangular FMCW LADAR Signals Denoising Method
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2501
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Wyszkowska, P.[Patrycja],
Duchnowski, R.[Robert],
Msplit Estimation with Local or Global Robustness Against Outliers:
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RS(16), No. 23, 2024, pp. 4512.
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2501
Lidar outliers.
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Liu, Z.[Zheng],
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PyramidPCD: A novel pyramid network for point cloud denoising,
PR(161), 2025, pp. 111228.
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WWW Link.
2502
Point cloud denoising, Feature pyramid, Transformer
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Lv, X.[Xin],
Wang, X.[Xiao],
Yang, X.M.[Xiao-Meng],
Xie, J.F.[Jun-Feng],
Mo, F.[Fan],
Xu, C.[Chaopeng],
Zhang, F.X.[Fang-Xv],
A Novel Photon-Counting Laser Point Cloud Denoising Method Based on
Spatial Distribution Hierarchical Clustering for Inland Lake Water
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2503
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Quan, S.[Siwen],
Zhao, H.[Hebin],
Zeng, Z.[Zhao],
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Pre-training meets iteration:
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WWW Link.
2503
Pre-training, Iteration, Denoising, Masked auto-encoder, Point cloud
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Chen, C.[Chao],
Liu, Y.S.[Yu-Shen],
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Learning Local Pattern Modularization for Point Cloud Reconstruction
from Unseen Classes,
ECCV24(XLVIII: 305-323).
Springer DOI
2412
BibRef
Vogel, M.[Mathias],
Tateno, K.[Keisuke],
Pollefeys, M.[Marc],
Tombari, F.[Federico],
Rakotosaona, M.J.[Marie-Julie],
Engelmann, F.[Francis],
P2P-Bridge: Diffusion Bridges for 3d Point Cloud Denoising,
ECCV24(II: 184-201).
Springer DOI
2412
BibRef
Zhu, S.J.[Sheng-Jie],
Ganesan, G.C.[Girish Chandar],
Kumar, A.[Abhinav],
Liu, X.M.[Xiao-Ming],
Replay: Remove Projective Lidar Depthmap Artifacts via Exploiting
Epipolar Geometry,
ECCV24(LXXXI: 393-411).
Springer DOI
2412
BibRef
Mao, A.[Aihua],
Yan, B.[Biao],
Ma, Z.J.[Zi-Jing],
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Denoising Point Clouds in Latent Space via Graph Convolution and
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CVPR24(5768-5777)
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WWW Link.
2410
Point cloud compression, Codes, Convolution, Source coding, Noise,
Neural networks, Noise reduction
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Han, H.Z.[Hao-Zheng],
Jin, X.[Xin],
Li, Z.H.[Zhi-Heng],
Denoising Point Clouds with Intensity and Spatial Features in Rainy
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ICIP23(3015-3019)
IEEE DOI
2312
BibRef
Zhao, Y.P.[Ya-Ping],
Zheng, H.[Haitian],
Wang, Z.[Zhongrui],
Luo, J.B.[Jie-Bo],
Lam, E.Y.[Edmund Y.],
Point Cloud Denoising Via Momentum Ascent in Gradient Fields,
ICIP23(161-165)
IEEE DOI Code:
WWW Link.
2312
BibRef
Mao, A.[Aihua],
Du, Z.H.[Zi-Hui],
Wen, Y.H.[Yu-Hui],
Xuan, J.[Jun],
Liu, Y.J.[Yong-Jin],
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows,
ECCV22(III:398-415).
Springer DOI
2211
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Schelling, M.[Michael],
Hermosilla, P.[Pedro],
Ropinski, T.[Timo],
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising,
CVPR22(661-670)
IEEE DOI
2210
Point cloud compression, Neural networks, Noise reduction,
Distortion, Cameras, Deep learning architectures and techniques,
Vision + graphics
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Luo, S.T.[Shi-Tong],
Hu, W.[Wei],
Score-Based Point Cloud Denoising,
ICCV21(4563-4572)
IEEE DOI
2203
Point cloud compression, Surface cleaning, Training,
Surface reconstruction, Noise reduction, Neural networks,
3D from multiview and other sensors
BibRef
Masuda, M.[Mana],
Hachiuma, R.[Ryo],
Fujii, R.[Ryo],
Saito, H.[Hideo],
Sekikawa, Y.[Yusuke],
Toward Unsupervised 3d Point Cloud Anomaly Detection Using
Variational Autoencoder,
ICIP21(3118-3122)
IEEE DOI
2201
Adaptation models, Image processing, Task analysis,
Anomaly detection, 3D point cloud, anomaly detection,
variational autoencoder
BibRef
Shabanov, A.,
Krotov, I.,
Chinaev, N.,
Poletaev, V.,
Kozlukov, S.,
Pasechnik, I.,
Yakupov, B.,
Sanakoyeu, A.,
Lebedev, V.,
Ulyanov, D.,
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D
sensors,
3DV20(743-752)
IEEE DOI
2102
Sensors, Cameras, Noise reduction,
Image color analysis, Calibration, Task analysis
BibRef
Zhou, H.,
Chen, K.,
Zhang, W.,
Fang, H.,
Zhou, W.,
Yu, N.,
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point
Clouds Defense,
ICCV19(1961-1970)
IEEE DOI
2004
computer graphics, cryptography, feature extraction,
Gaussian processes, image classification, image denoising, Measurement
BibRef
Sterzentsenko, V.,
Saroglou, L.,
Chatzitofis, A.,
Thermos, S.,
Zioulis, N.,
Doumanoglou, A.,
Zarpalas, D.,
Daras, P.,
Self-Supervised Deep Depth Denoising,
ICCV19(1242-1251)
IEEE DOI
2004
Code, Depth Denoising.
WWW Link. cameras, convolutional neural nets,
data acquisition, image colour analysis, image denoising, Color
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Hui, Z.,
Cheng, P.,
Wang, L.,
Xia, Y.,
Hu, H.,
Li, X.,
A Novel Denoising Algorithm for Airborne Lidar Point Cloud Based On
Empirical Mode Decomposition,
Laser19(1021-1025).
DOI Link
1912
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Mugner, E.,
Seube, N.,
Denoising of 3d Point Clouds,
LC3D19(217-224).
DOI Link
1912
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Sarkar, K.,
Bernard, F.[Florian],
Varanasi, K.,
Theobalt, C.[Christian],
Stricker, D.,
Structured Low-Rank Matrix Factorization for Point-Cloud Denoising,
3DV18(444-453)
IEEE DOI
1812
approximation theory, image denoising, image reconstruction,
image representation, iterative methods,
3D patches
BibRef
Charron, N.,
Phillips, S.,
Waslander, S.L.,
De-noising of Lidar Point Clouds Corrupted by Snowfall,
CRV18(254-261)
IEEE DOI
1812
Laser radar, Snow, Noise reduction,
Smoothing methods, Image segmentation,
snow noise removal
BibRef
Kim, Y.S.,
Closed-Form Solution of Simultaneous Denoising and Hole Filling of
Depth Image,
ICIP18(968-972)
IEEE DOI
1809
Filtering, Image reconstruction, Image color analysis, Kernel,
Filling, Cameras, Color, Depth recovery, denoising, hole filling,
time-of-flight depth
BibRef
Sarkar, K.[Kripasindhu],
Hampiholi, B.[Basavaraj],
Varanasi, K.[Kiran],
Stricker, D.[Didier],
Learning 3D Shapes as Multi-layered Height-Maps Using 2D Convolutional
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ECCV18(XVI: 74-89).
Springer DOI
1810
BibRef
Earlier: A1, A3, A4, Only:
3D Shape Processing by Convolutional Denoising Autoencoders on Local
Patches,
WACV18(1925-1934)
IEEE DOI
1806
computational geometry, convolution, feedforward neural nets,
image coding, image denoising, image reconstruction,
BibRef
Liao, X.,
Zhang, X.,
Multi-scale mutual feature convolutional neural network for depth
image denoise and enhancement,
VCIP17(1-4)
IEEE DOI
1804
cameras, feature extraction, image colour analysis,
image denoising, image enhancement,
mutual feature
BibRef
Jaiswal, M.S.[Mayoore S.],
Wang, Y.Y.[Yu-Ying],
Sun, M.T.[Ming-Ting],
Object Boundary Based Denoising for Depth Images,
ICIAR17(125-133).
Springer DOI
1706
BibRef
Wolff, K.,
Kim, C.,
Zimmer, H.,
Schroers, C.,
Botsch, M.,
Sorkine-Hornung, O.,
Sorkine-Hornung, A.,
Point Cloud Noise and Outlier Removal for Image-Based 3D
Reconstruction,
3DV16(118-127)
IEEE DOI
1701
Image reconstruction
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Outpainting, Extrapolation .