Journal Home > Volume 9 , Issue 2
Computational Visual Media 2023, 9(2): 249-263 https://doi.org/10.1007/s41095-022-0278-4
Research Article | Open Access | Issue | Published: 03 January 2023

Towards uniform point distribution in feature-preserving point cloud filtering

Show Author's Information Shuaijun Chen1,*,  Jinxi Wang2,*,  Wei Pan3,  Shang Gao1,  Meili Wang2( ),  Xuequan Lu1( )
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
College of Information Engineering, Northwest A&F University, Yangling 712100, China
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China

*Shuaijun Chen and Jinxi Wang contributed equally to this work.

Keywords:
point distribution, point cloud filtering, feature preservation
Cite this article:
Chen S, Wang J, Pan W, et al. Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 2023, 9(2): 249-263. https://doi.org/10.1007/s41095-022-0278-4
Download citation
EndNote(RIS)
BibTeX

7208

Views

138

Downloads

Citations

6

Crossref

4

WoS

5

Scopus

0

CSCD

Abstract Full text About this article

While a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term aims toapproximate the noisy surfaces while preserving geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.


menu
Abstract
Full text
Outline
About this article

Towards uniform point distribution in feature-preserving point cloud filtering

Show Author's information Shuaijun Chen1,*, Jinxi Wang2,*, Wei Pan3, Shang Gao1, Meili Wang2( ), Xuequan Lu1( )
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
College of Information Engineering, Northwest A&F University, Yangling 712100, China
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China

*Shuaijun Chen and Jinxi Wang contributed equally to this work.

Abstract

While a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term aims toapproximate the noisy surfaces while preserving geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.

Keywords: point distribution, point cloud filtering, feature preservation

References(36)

[1]
Huang, H.; Wu, S. H.; Gong, M. L.; Cohen-Or, D.; Ascher, U.; Zhang, H. R. Edge-aware point set resampling. ACM Transactions on Graphics Vol. 32, No. 1, Article No. 9, 2013.
DOI Google Scholar
[2]
Kazhdan, M.; Hoppe, H. Screened Poisson surface reconstruction. ACM Transactions on Graphics Vol. 32, No. 3, Article No. 29, 2013.
DOI Google Scholar
[3]
Öztireli, A. C.; Guennebaud, G.; Gross, M. Feature preserving point set surfaces based on non-linear kernel regression. Computer Graphics Forum Vol. 28, No. 2, 493–501, 2009.
DOI Google Scholar
[4]
Lu, X. Q.; Chen, H. H.; Yeung, S. K.; Deng, Z. G.; Chen, W. Z. Unsupervised articulated skeleton extraction from point set sequences captured by a single depth camera. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32, No. 1, 7226–7234, 2018.
DOI Google Scholar
[5]
Lu, X. Q.; Deng, Z. G.; Luo, J.; Chen, W. Z.; Yeung, S. K.; He, Y. 3D articulated skeleton extraction using a single consumer-grade depth camera. Computer Vision and Image Understanding Vol. 188, 102792, 2019.
DOI Google Scholar
[6]
Lu, X. Q.; Wang, Z. H.; Xu, M. L.; Chen, W. Z.; Deng, Z. G. A personality model for animating heterogeneous traffic behaviors. Computer Animation and Virtual Worlds Vol. 25, Nos. 3–4, 361–371, 2014.
DOI Google Scholar
[7]
Pei, Y.; Huang, Z.; Yu, W.; Wang, M.; Lu, X. A Cascaded approach for keyframes extraction from videos. In: Computer Animation and Social Agents. Communications in Computer and Information Science, Vol. 1300. Springer Cham, 73–81, 2020.
[8]
Lipman, Y.; Cohen-Or, D.; Levin, D.; Tal-Ezer, H. Parameterization-free projection for geometry reconstruction. ACM Transactions on Graphics Vol. 26, No. 3, 22–es, 2007.
DOI Google Scholar
[9]
Huang, H.; Li, D.; Zhang, H.; Ascher, U.; Cohen-Or, D. Consolidation of unorganized point clouds for surface reconstruction. ACM Transactions on Graphics Vol. 28, No. 5, 1–7, 2009.
DOI Google Scholar
[10]
Preiner, R.; Mattausch, O.; Arikan, M.; Pajarola, R.; Wimmer, M. Continuous projection for fast L1 reconstruction. ACM Transactions on Graphics Vol. 33, No. 4, Article No. 47, 2014.
DOI Google Scholar
[11]
Lu, X.; Wu, S.; Chen, H.; Yeung, S.-K.; Chen, W.; Zwicker, M. GPF: GMM-inspired feature-preserving point set filtering. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 8, 2315–2326, 2018.
DOI Google Scholar
[12]
Rakotosaona, M.-J.; La Barbera, V.; Guerrero, P.; Mitra, N. J.; Ovsjanikov, M. PointCleanNet: Learning to denoise and remove outliers from dense point clouds. Computer Graphics Forum Vol. 39, 185–203, 2020.
DOI Google Scholar
[13]
Roveri, R.; Öztireli, A. C.; Pandele, I.; Gross, M. PointProNets: Consolidation of point clouds with convolutional neural networks. Computer Graphics Forum Vol. 37, No. 2, 87–99, 2018.
DOI Google Scholar
[14]
Zhang, D.; Lu, X.; Qin, H.; He, Y. Pointfilter: Point cloud filtering via encoder–decoder modeling. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 3, 2015–2027, 2021.
DOI Google Scholar
[15]
Hoppe, H.; DeRose, T.; Duchamp, T.; McDonald, J.; Stuetzle, W. Surface reconstruction from unorganized points. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, 71–78, 1992.
DOI
[16]
Lu, X. Q.; Schaefer, S.; Luo, J.; Ma, L. Z.; He, Y. Low rank matrix approximation for 3D geometry filtering. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 4, 1835–1847, 2022.
DOI Google Scholar
[17]
Liao, B.; Xiao, C. X.; Jin, L. Q.; Fu, H. B. Efficient feature-preserving local projection operator for geometry reconstruction. Computer-Aided Design Vol. 45, No. 5, 861–874, 2013.
DOI Google Scholar
[18]
Levin, D. The approximation power of moving least-squares. Mathematics of Computation Vol. 67, No. 224, 1517–1531, 1998.
DOI Google Scholar
[19]
Levin, D. Mesh-independent surface interpolation. In: Geometric Modeling for Scientific Visualization. Mathematics and Visualization. Brunnett, G.; Hamann, B.; Müller, H.; Linsen, L. Eds. Springer Berlin Heidelberg, 37–49, 2004.
DOI
[20]
Alexa, M.; Behr, J.; Cohen-Or, D.; Fleishman, S.; Levin, D.; Silva, C. T. Computing and rendering point set surfaces. IEEE Transactions on Visualization and Computer Graphics Vol. 9, No. 1, 3–15, 2003.
DOI Google Scholar
[21]
Guennebaud, G.; Gross, M. Algebraic point set surfaces. ACM Transactions on Graphics Vol. 26, No. 3, 23–es, 2007.
DOI Google Scholar
[22]
Rusu, R. B.; Blodow, N.; Marton, Z.; Soos, A.; Beetz, M. Towards 3D object maps for autonomous household robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 3191–3198, 2007.
[23]
Liu, Z.; Xiao, X. W.; Zhong, S. S.; Wang, W. N.; Li, Y. L.; Zhang, L.; Xie, Z. A feature-preserving framework for point cloud denoising. Computer-Aided Design Vol. 127, 102857, 2020.
DOI Google Scholar
[24]
Deschaud, J.-E.; Goulette, F. Point cloud nonlocal denoising using local surface descriptor similarity. IAPRS Vol. 38, No. 3A, 109–114, 2010.
Google Scholar
[25]
Digne, J. Similarity based filtering of point clouds. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 73–79, 2012.
DOI
[26]
Zeng, J.; Cheung, G.; Ng, M.; Pang, J. H.; Yang, C. 3D point cloud denoising using graph Laplacian regularization of a low dimensional manifold model. IEEE Transactions on Image Processing Vol. 29, 3474–3489, 2020.
DOI Google Scholar
[27]
Chen, H.; Wei, M.; Sun, Y.; Xie, X.; Wang, J. Multi-patch collaborative point cloud denoising via low-rank recovery with graph constraint. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 11,3255–3270, 2020.
DOI Google Scholar
[28]
Liu, Y.; Guo, J.; Benes, B.; Deussen, O.; Zhang, X.; Huang, H. TreePartNet: Neural decomposition of point clouds for 3D tree reconstruction. ACM Transactions on Graphics Vol. 40, No. 6, Article No. 232, 2021.
DOI Google Scholar
[29]
Erler, P.; Guerrero, P.; Ohrhallinger, S.; Mitra, N. J.; Wimmer, M. POINTS2SURF Learning implicit surfaces from point clouds. In: Computer Vision – ECCV 2020. Lecture Notes in Computer Science, Vol. 12350. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 108–124, 2020.
DOI
[30]
Duan, C.; Chen, S. Kovacevic, J. 3D point cloud denoising via deep neural network based local surface estimation. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 8553–8557, 2019.
DOI
[31]
Yu, L.; Li, X.; Fu, C. W.; Cohen-Or, D.; Heng, P. A. EC-Net: An edge-aware point set consolidation network. In: Computer Vision – ECCV 2018. Lecture Notes in Computer Science, Vol. 11211. Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y. Eds. Springer Cham, 398–414, 2018.
[32]
Lu, D. N.; Lu, X. Q.; Sun, Y. X.; Wang, J. Deep feature-preserving normal estimation for point cloud filtering. Computer-Aided Design Vol. 125, 102860, 2020.
DOI Google Scholar
[33]
Charles, R. Q.; Su, H.; Kaichun, M.; Guibas, L. J. PointNet: Deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 77–85, 2017.
DOI
[34]
Yu, L. Q.; Li, X. Z.; Fu, C. W.; Cohen-Or, D.; Heng, P. A. PU-net: Point cloud upsampling network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2790–2799, 2018.
[35]
Guerrero, P.; Kleiman, Y.; Ovsjanikov, M.; Mitra, N. J. PCPNet: Learning local shape properties from raw point clouds. Computer Graphics Forum Vol. 37, No. 2, 75–85, 2018.
DOI Google Scholar
[36]
Casajus, P. H.; Ritschel, T.; Ropinski, T. Total denoising: Unsupervised learning of 3D point cloud cleaning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 52–60, 2019.
DOI
Publication history
Copyright
Rights and permissions

Publication history

Received: 10 January 2022
Accepted: 20 February 2022
Published: 03 January 2023
Issue date: June 2023

Copyright

© The Author(s) 2022.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.

Return