11.14.3.9.13 Denoising, Range Images, Range, Depth Data

Chapter Contents (Back)
Depth Denoising. Point Cloud Denoising. LiDAR. Noise.
See also Inpainting, Inpainting Range Images, Range, Depth Data.

Woiselle, A., Starck, J.L., Fadili, J.,
3-D Data Denoising and Inpainting with the Low-Redundancy Fast Curvelet Transform,
JMIV(39), No. 2, February 2011, pp. 121-139.
WWW Link. 1103
BibRef

Belhedi, A., Bartoli, A., Bourgeois, S., Gay-Bellile, V., Hamrouni, K., Sayd, P.,
Noise modelling in time-of-flight sensors with application to depth noise removal and uncertainty estimation in three-dimensional measurement,
IET-CV(9), No. 6, 2015, pp. 967-977.
DOI Link 1512
Gaussian distribution BibRef

Papari, G., Idowu, N., Varslot, T.,
Fast Bilateral Filtering for Denoising Large 3D Images,
IP(26), No. 1, January 2017, pp. 251-261.
IEEE DOI 1612
Gaussian distribution BibRef

Gao, Z., Li, Q., Zhai, R., Shan, M., Lin, F.,
Adaptive and Robust Sparse Coding for Laser Range Data Denoising and Inpainting,
CirSysVideo(26), No. 12, December 2016, pp. 2165-2175.
IEEE DOI 1612
Dictionaries BibRef

Garduņo-Ramon, M.A.[Marco Antonio], Terol-Villalobos, I.R.[Ivan R.], Osornio-Rios, R.A.[Roque A.], Morales-Hernandez, L.A.[Luis A.],
Methodology for filtering of depth maps based on the MCbR algorithm supported by color, shape and neighboring features,
SP:IC(70), 2019, pp. 220-232.
Elsevier DOI 1812
Time-of-flight, Inpainting, Closing by reconstruction, Mathematical morphology, Filtering, Noise classifier, Template matching BibRef

Zheng, Y.L.[Ying-Long], Li, G.Q.[Gui-Qing], Wu, S.H.[Shi-Hao], Liu, Y.X.[Yu-Xin], Gao, Y.F.[Yue-Fang],
Guided point cloud denoising via sharp feature skeletons,
VC(33), No. 6-8, June 2017, pp. 857-867.
WWW Link. 1706
BibRef

Mukherjee, P.S.[Partha Sarathi],
A multi-resolution and adaptive 3-D image denoising framework with applications in medical imaging,
SIViP(11), No. 7, October 2017, pp. 1379-1387.
WWW Link. 1708
BibRef

Cheng, Y.[Yang], Cao, J.[Jie], Hao, Q.[Qun], Xiao, Y.Q.[Yu-Qing], Zhang, F.H.[Fang-Hua], Xia, W.Z.[Wen-Ze], Zhang, K.[Kaiyu], Yu, H.Y.[Hao-Yong],
A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Li, H.X.[Hong-Xu], Chang, J.H.[Jian-Hua], Xu, F.[Fan], Liu, Z.X.[Zhen-Xing], Yang, Z.B.[Zhen-Bo], Zhang, L.[Luyao], Zhang, S.Y.[Shu-Yi], Mao, R.X.[Ren-Xiang], Dou, X.L.[Xiao-Lei], Liu, B.G.[Bing-Gang],
Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Gao, Z., Ji, H.,
Transform Learning Based Sparse Coding for LiDAR Data Denoising,
SPLetters(26), No. 3, March 2019, pp. 480-484.
IEEE DOI 1903
electrical engineering computing, iterative methods, learning (artificial intelligence), transform learning BibRef

Zeng, J., Cheung, G., Ng, M., Pang, J., Yang, C.,
3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model,
IP(29), 2020, pp. 3474-3489.
IEEE DOI 2002
Graph signal processing, point cloud denoising, low-dimensional manifold BibRef

Dinesh, C., Cheung, G., Bajic, I.V.,
Point Cloud Denoising via Feature Graph Laplacian Regularization,
IP(29), 2020, pp. 4143-4158.
IEEE DOI 2002
3D point cloud, graph signal processing, denoising, convex optimization, graph Laplacian regularizer BibRef

Casajus, P.H., Ritschel, T., Ropinski, T.,
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning,
ICCV19(52-60)
IEEE DOI 2004
image denoising, unsupervised learning, unsupervised 3D point cloud denoising, unsupervised learning, Training BibRef

Eldesokey, A.[Abdelrahman], Felsberg, M.[Michael], Khan, F.S.[Fahad Shahbaz],
Confidence Propagation through CNNs for Guided Sparse Depth Regression,
PAMI(42), No. 10, October 2020, pp. 2423-2436.
IEEE DOI 2009
Convolution, Sensors, Task analysis, Cameras, Autonomous vehicles, Reliability, Sparse data, CNNs, confidence propagation BibRef

Eldesokey, A., Felsberg, M., Holmquist, K., Persson, M.,
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End,
CVPR20(12011-12020)
IEEE DOI 2008
Uncertainty, Task analysis, Probabilistic logic, Measurement uncertainty, Noise measurement, Convolution BibRef

Ibrahim, M.M.[Mostafa M.], Liu, Q.[Qiong], Yang, Y.[You],
Adaptive colour-guided non-local means algorithm for compound noise reduction of depth maps,
IET-IPR(14), No. 12, October 2020, pp. 2768-2779.
DOI Link 2010
BibRef

Dong, G.T.[Guan-Ting], Zhang, Y.Y.[Yue-Yi], Xiong, Z.W.[Zhi-Wei],
Spatial Hierarchy Aware Residual Pyramid Network for Time-of-flight Depth Denoising,
ECCV20(XXIV:35-50).
Springer DOI 2012
BibRef

Valsesia, D.[Diego], Fracastoro, G.[Giulia], Magli, E.[Enrico],
Learning Localized Representations of Point Clouds With Graph-Convolutional Generative Adversarial Networks,
MultMed(23), 2021, pp. 402-414.
IEEE DOI 2012
Convolution, Generators, Generative adversarial networks, Feature extraction, Data models, Generative adversarial networks, point clouds BibRef

Pistilli, F.[Francesca], Fracastoro, G.[Giulia], Valsesia, D.[Diego], Magli, E.[Enrico],
Learning Graph-convolutional Representations for Point Cloud Denoising,
ECCV20(XX:103-118).
Springer DOI 2011
BibRef

Irfan, M.A.[Muhammad Abeer], Magli, E.[Enrico],
Exploiting color for graph-based 3D point cloud denoising,
JVCIR(75), 2021, pp. 103027.
Elsevier DOI 2103
Point cloud denoising, Color denoising, Convex optimization, Tikhonov regularization, Total variation, Graph signal processing BibRef

Hu, W.[Wei], Hu, Q.J.[Qian-Jiang], Wang, Z.[Zehua], Gao, X.[Xiang],
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance,
IP(30), 2021, pp. 6168-6183.
IEEE DOI 2107
Noise reduction, Manifolds, Optimization, Minimization, Laser radar, Surface reconstruction, Dynamic point cloud denoising, temporal consistency BibRef

Zhou, Y.[Yiyao], Chen, R.[Rui], Zhao, Y.Q.[Yi-Qiang], Ai, X.D.[Xi-Ding], Zhou, G.Q.[Guo-Qing],
Point cloud denoising using non-local collaborative projections,
PR(120), 2021, pp. 108128.
Elsevier DOI 2109
Point cloud denoising, Adaptive curvature threshold, Structure-aware descriptor, Projective height vector, Improved weighted nuclear norm minimization BibRef

Gao, R.[Rui], Park, J.[Jisun], Hu, X.H.[Xiao-Hang], Yang, S.J.[Seung-Jun], Cho, K.[Kyungeun],
Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Gao, R.[Rui], Li, M.Y.[Meng-Yu], Yang, S.J.[Seung-Jun], Cho, K.[Kyungeun],
Reflective Noise Filtering of Large-Scale Point Cloud Using Transformer,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zheng, Z.[Zhen], Zha, B.T.[Bing-Ting], Zhou, Y.[Yu], Huang, J.[Jinbo], Xuchen, Y.S.[You-Shi], Zhang, H.[He],
Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Chen, H.H.[Hong-Hua], Wei, Z.Y.[Ze-Yong], Li, X.Z.[Xian-Zhi], Xu, Y.B.[Ya-Bin], Wei, M.Q.[Ming-Qiang], Wang, J.[Jun],
RePCD-Net: Feature-Aware Recurrent Point Cloud Denoising Network,
IJCV(130), No. 3, March 2022, pp. 615-629.
Springer DOI 2203
BibRef

Salehi, B.[Bahram], Jarahizadeh, S.[Sina], Sarafraz, A.[Amin],
An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Zhou, H.R.[Hao-Ran], Chen, H.H.[Hong-Hua], Zhang, Y.K.[Ying-Kui], Wei, M.Q.[Ming-Qiang], Xie, H.R.[Hao-Ran], Wang, J.[Jun], Lu, T.[Tong], Qin, J.[Jing], Zhang, X.P.[Xiao-Ping],
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds,
PAMI(45), No. 1, January 2023, pp. 946-963.
IEEE DOI 2212
Estimation, Point cloud compression, Noise measurement, Feature extraction, Task analysis, Surface reconstruction, point cloud denoising BibRef

Ma, R.[Rujia], Kong, W.[Wei], Chen, T.[Tao], Shu, R.[Rong], Huang, G.[Genghua],
KNN Based Denoising Algorithm for Photon-Counting LiDAR: Numerical Simulation and Parameter Optimization Design,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Si, S.M.[Shu-Ming], Hu, H.[Han], Ding, Y.L.[Yu-Lin], Yuan, X.[Xuekun], Jiang, Y.[Ying], Jin, Y.[Yigao], Ge, X.M.[Xu-Ming], Zhang, Y.T.[Ye-Ting], Chen, J.[Jie], Guo, X.[Xiaocui],
Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Li, Y.[Ying], Sheng, H.K.[Huan-Kun],
A single-stage point cloud cleaning network for outlier removal and denoising,
PR(138), 2023, pp. 109366.
Elsevier DOI 2303
Point cloud cleaning, Denoising, Outlier removal, Neural networks BibRef

Wang, X.T.[Xing-Tao], Fan, X.P.[Xiao-Peng], Zhao, D.B.[De-Bin],
PointFilterNet: A Filtering Network for Point Cloud Denoising,
CirSysVideo(33), No. 3, March 2023, pp. 1276-1290.
IEEE DOI 2303
Point cloud compression, Noise reduction, Noise measurement, Deep learning, Image reconstruction, Training, HVS BibRef

Hu, X.[Xin], Wei, X.[Xin], Sun, J.[Jian],
A Noising-Denoising Framework for Point Cloud Upsampling via Normalizing Flows,
PR(140), 2023, pp. 109569.
Elsevier DOI 2305
Point cloud, Arbitrary ratio upsampling, Normalizing flows BibRef

Li, Y.H.[Yu-Hao], Zou, X.[Xianghong], Li, T.[Tian], Sun, S.[Sihan], Wang, Y.[Yuan], Liang, F.[Fuxun], Li, J.P.[Jiang-Ping], Yang, B.S.[Bi-Sheng], Dong, Z.[Zhen],
MuCoGraph: A multi-scale constraint enhanced pose-graph framework for MLS point cloud inconsistency correction,
PandRS(204), 2023, pp. 421-441.
Elsevier DOI 2310
Mobile laser scanning point cloud, Position inconsistency correction, Multi-scale constraint, Graph optimization BibRef

Wang, X.T.[Xing-Tao], Cui, W.X.[Wen-Xue], Xiong, R.Q.[Rui-Qin], Fan, X.P.[Xiao-Peng], Zhao, D.B.[De-Bin],
FCNet: Learning Noise-Free Features for Point Cloud Denoising,
CirSysVideo(33), No. 11, November 2023, pp. 6288-6301.
IEEE DOI 2311
BibRef

Sun, Y.[Yefa], Wang, J.[Jinli],
Nonparametric point cloud filter,
IET-IPR(18), No. 2, 2024, pp. 388-402.
DOI Link 2402
adaptive filters, filtering theory BibRef


Han, H.Z.[Hao-Zheng], Jin, X.[Xin], Li, Z.H.[Zhi-Heng],
Denoising Point Clouds with Intensity and Spatial Features in Rainy Weather,
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.[Zihui], 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
BibRef

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 BibRef

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 BibRef

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
BibRef

Mugner, E., Seube, N.,
Denoising of 3d Point Clouds,
LC3D19(217-224).
DOI Link 1912
BibRef

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 Networks,
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 .


Last update:Mar 16, 2024 at 20:36:19