Efficient Filtering and Smoothing Algorithm For Train Key Components Based on Scattered Point Clouds

Ni Zeng; Jinlong Li; Xiaorong Gao; Yu Zhang; Lin Luo

doi:10.3788/LOP221987

[1] Ding S S, Chen D W, Liu J L. Research, development and prospect of China high-speed train[J]. Chinese Journal of Theoretical and Applied Mechanics, 53, 35-50(2021).

[2] Li W G, Mei Y, Fan X et al. Railway track detection based on vehicle laser point cloud[J]. Chinese Journal of Lasers, 49, 0410002(2022).

[3] Yao L Y, Qiu J H, Gao S B et al. Defect detection in high-speed railway overhead contact system: importance, challenges, and methods[C], 76-80(2021).

[4] Wang Z X, Zhang Y, Wang N et al. Locking wire fracture classification algorithm based on convolutional neural networks[J]. Journal of the China Railway Society, 44, 27-33(2022).

[5] Zhao B, Dai M R, Li P et al. Research on defect detection of railway key components based on deep learning[J]. Journal of the China Railway Society, 41, 67-73(2019).

[6] Li C Q, Wei Z Z, Xing J. Online inspection system for the automatic detection of bolt defects on a freight train[J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230, 1213-1226(2016).

[7] Li W G, Fan X, Mei Y et al. Road marking extraction method based on vehicle laser point cloud[J]. Chinese Journal of Lasers, 49, 0710001(2022).

[8] Lin S, Xu C, Chen L P et al. LiDAR point cloud recognition of overhead catenary system with deep learning[J]. Sensors, 20, 2212(2020).

[9] Han X F, Jin J S, Wang M J et al. A review of algorithms for filtering the 3D point cloud[J]. Signal Processing: Image Communication, 57, 103-112(2017).

[10] Yuan H, Pang J K, Mo J W. Denoising algorithm for bilateral filtered point cloud based on noise classification[J]. Journal of Computer Applications, 35, 2305-2310(2015).

[11] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C], 839-846(1998).

[12] Julie D, Carlo D F. The bilateral filter for point clouds[J]. Image Processing on Line, 7, 278-287(2017).

[13] Zhang F, Zhang C, Yang H M et al. Point cloud denoising with principal component analysis and a novel bilateral filter[J]. Traitement Du Signal, 36, 393-398(2019).

[14] Zhou L, Sun G X, Li Y et al. Point cloud denoising review: from classical to deep learning-based approaches[J]. Graphical Models, 121, 101140(2022).

[15] Levin D. The approximation power of moving least-squares[J]. Mathematics of Computation, 67, 1517-1531(1998).

[16] Alexa M, Behr J, Cohen-Or D et al. Computing and rendering point set surfaces[J]. IEEE Transactions on Visualization and Computer Graphics, 9, 3-15(2003).

[17] Lipman Y, Cohen-Or D, Levin D et al. Parameterization-free projection for geometry reconstruction[J]. ACM Transactions on Graphics, 26, 22(2007).

[18] Han X F, Jin J S, Wang M J et al. Guided 3D point cloud filtering[J]. Multimedia Tools and Applications, 77, 17397-17411(2018).

[19] He K M, Sun J, Tang X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1397-1409(2013).

[20] Wei Y, Xiao Y, Yan C et al. Data optimization of pavement 3D reconstruction based on point preprocessing[J]. Journal of Jilin University (Engineering and Technology Edition), 50, 987-997(2020).

[21] Javaheri A, Brites C, Pereira F et al. Subjective and objective quality evaluation of 3D point cloud denoising algorithms[C](2017).