[1] Li P F, Wu H E, Jing J F et al. Noise classification denoising algorithm for point cloud model[J]. Computer Engineering and Applications, 52, 188-192(2016).
[2] Zhai J L. Research on spatial scattered points cloud denoising, streamlining and surface reconstruction algorithm[D](2015).
[3] Liu L H, Zhao F Q, Tang H et al. A denoising method for point cloud of cultural relics with geometric feature preservation[J]. Journal of Data Acquisition and Processing, 35, 373-380(2020).
[4] Yang Y, Li M. A point cloud denoising method based on a hybrid filtering and density clustering algorithm[J]. Metrology & Measurement Technique, 47, 24-27(2020).
[5] Liu Y, Sun S Y. Laser point cloud denoising based on principal component analysis and surface fitting[J]. Laser Technology, 44, 497-502(2020).
[6] Zeng J, Cheung G, Ng M et al. 3D point cloud denoising using graph Laplacian regularization of a low dimensional manifold model[J]. IEEE Transactions on Image Processing, 29, 3474-3489(2020).
[7] Tang G, Deng X S, Wang Q Y. Research on point cloud filtering algorithm based on density clustering[J/OL]. Progress in laser and optoelectronics, 1-17. http://kns.cnki.net/kcms/detail/31.1690.tn.20210716.1534.012.html
[8] Han H Y, Zhang Y, Han X. Improved laser point cloud filtering algorithm[J]. Laser & Optoelectronics Progress, 58, 2010001(2021).
[9] Cao X, Lin Z X, Song S L et al. Multispectral LiDAR point cloud denoising based on color clustering[J]. Laser & Optoelectronics Progress, 58, 1228002(2021).
[10] Qiao J F, Han H G. Optimal structure design for RBFNN structure[J]. Acta Automatica Sinica, 36, 865-872(2010).
[11] Zhou Z H, Chen S F. Neural network ensemble[J]. Chinese Journal of Computers, 25, 1-8(2002).
[12] Pistilli F, Fracastoro G, Valsesia D et al. Learning graph-convolutional representations for point cloud denoising[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020, 12365, 103-118(2020).
[13] Almonacid J, Cintas C, Derieux C et al. Point cloud denoising using deep learning[C], 18327532(2018).
[14] Heinzler R, Piewak F, Schindler P et al. CNN-based lidar point cloud de-noising in adverse weather[J]. IEEE Robotics and Automation Letters, 5, 2514-2521(2020).
[15] Duan C J, Chen S H, Kovacevic J. 3D point cloud denoising via deep neural network based local surface estimation[C], 8553-8557(2019).
[16] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).
[17] Rakotosaona M J, La Barbera V, Guerrero P et al. PointCleanNet: learning to denoise and remove outliers from dense point clouds[J]. Computer Graphics Forum, 39, 185-203(2020).
[18] Guerrero P, Kleiman Y, Ovsjanikov M et al. PCPNet learning local shape properties from raw point clouds[J]. Computer Graphics Forum, 37, 75-85(2018).
[19] Casajus P H, Ritschel T, Ropinski T. Total denoising: unsupervised learning of 3D point cloud cleaning[C], 52-60(2019).
[20] Boiko A A, Malashin R O. Single-frame Noise2Noise: method of training a neural network without using reference data for video sequence image enhancement[J]. Journal of Optical Technology, 87, 567-573(2020).
[21] Lehtinen J, Munkberg J, Hasselgren J et al. Noise2Noise: learning image restoration without clean data[EB/OL]. https://arxiv.org/abs/1803.04189
[22] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).
[23] Wei L Y. Parallel Poisson disk sampling[J]. ACM Transactions on Graphics, 27, 1-9(2008).
[24] Fan H Q, Su H, Guibas L. A point set generation network for 3D object reconstruction from a single image[C], 2463-2471(2017).