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Guided 3D point cloud filtering

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Abstract

3D point cloud has gained significant attention in recent years. However, raw point clouds captured by 3D sensors are unavoidably contaminated with noise resulting in detrimental efforts on the practical applications. Although many widely used point cloud filters such as normal-based bilateral filter, can produce results as expected, they require a higher running time. Therefore, inspired by guided image filter, this paper takes the position information of the point into account to derive the linear model with respect to guidance point cloud and filtered point cloud. Experimental results show that the proposed algorithm, which can successfully remove the undesirable noise while offering better performance in feature-preserving, is significantly superior to several state-of-the-art methods, particularly in terms of efficiency.

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References

  1. Aldoma A, Marton ZC, Tombari F, Wohlkinger W, Potthast C, Zeisl B, Rusu RB, Gedikli S, Vincze M (2012) Tutorial: point cloud library: three-dimensional object recognition and 6 DOF pose estimation. IEEE Robot Autom Mag 19(3):80–91

    Article  Google Scholar 

  2. Alexa M, Behr J, Cohenor D, Fleishman S, Levin D, Silva CT (2003) Computing and rendering point set surfaces. IEEE Trans Vis Comput Graph 9(1):3–15

    Article  Google Scholar 

  3. Fleishman S, Drori I, Cohen-Or D (2003) Bilateral mesh denoising. Acm Trans Graph 22(3):950–953

    Article  Google Scholar 

  4. Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Trans Cybern 43(5):1318–1334

    Article  Google Scholar 

  5. Han XF, Jin JS, Wang MJ, Jiang W, Gao L, Xiao L (2017) A review of algorithms for filtering the 3D point cloud. Signal Process Image Commun 57:103–112

    Article  Google Scholar 

  6. Han XF, Jin JS, Wang MJ, Jiang W (2017) Iterative guidance normal filter for point cloud. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5258-9

  7. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  8. Hu G, Peng Q, Forrest AR (2006) Mean shift denoising of point-sampled surfaces. Vis Comput 22(3):147–157

    Article  Google Scholar 

  9. Huang H, Li D, Zhang H, Ascher U, Cohen-Or D (2009) Consolidation of unorganized point clouds for surface reconstruction. Acm Trans Graph 28(5):89–97

    Article  Google Scholar 

  10. Huang H, Wu S, Gong M, Cohen-Or D, Ascher U, Zhang H (2013) Edge-aware point set resampling. Acm Trans Graph 32(1):60–61

    Article  MATH  Google Scholar 

  11. Huhle B, Schairer T, Jenke P, Strasser W (2008) Robust non-local denoising of colored depth data. In: IEEE Cvpr workshop on time of flight camera based computer vision, pp 1–7

  12. Jenke P, Wand M, Bokeloh M, Schilling A et al (2006) Bayesian point cloud reconstruction. Comput Graph Forum 25(3):379–388

    Article  Google Scholar 

  13. Jones TR, Durand F, Desbrun M (2003) Non-iterative, feature-preserving mesh smoothing. Acm Trans Graph 22(3):943–949

    Article  Google Scholar 

  14. Jones TR, Durand F, Zwicker M (2004) Normal improvement for point rendering. IEEE Comput Graph Appl 24(4):53–56

    Article  Google Scholar 

  15. Kobbelt L, Botsch M (2004) Point-based computer graphics. Comput Graph 28(6):801–814

    Article  Google Scholar 

  16. Lee KW, Wang WP (2005) Feature-preserving mesh denoising via bilateral normal filtering. In: International conference on computer aided design and computer graphics, pp 275–280

  17. Liu S, Chan KC, Wang CCL (2012) Iterative consolidation of unorganized point clouds. IEEE Comput Graph Appl 32(3):70–83

    Article  Google Scholar 

  18. Ma S, Zhou C, Zhang L, Hong W (2014) Depth image denoising and key points extraction for manipulation plane detection. In: World Congress on intelligent control and automation, pp 3315– 3320

  19. Miropolsky A, Fischer A (2004) Reconstruction with 3D geometric bilateral filter. In: ACM Symposium on solid modeling and applications, pp 225–229

  20. Moorfield B, Haeusler R, Klette R (2015) Bilateral filtering of 3D point clouds for refined 3d roadside reconstructions? Lect Notes Comput Sci 9257:394–402

    Article  MathSciNet  Google Scholar 

  21. Nasab SE, Ghaleh SF, Ramezanpour S, Kasaei S (2014) Permutohedral lattice in 3D point cloud processing. In: International symposium on telecommunications

  22. Paris S (2007) A gentle introduction to bilateral filtering and its applications. In: SIGGRAPH. ACM, pp 853–879

  23. Park J, Kim H, Tai YW, Brown MS, Kweon I (2011) High quality depth map upsampling for 3D-TOF cameras. In: CVPR. IEEE, pp 1623–1630

  24. Rangel JC, Morell V, Cazorla M, Orts-Escolano S, Garca-Rodrguez J (2016) Object recognition in noisy RGB-D data using GNG. Formal Pattern Anal Appl 1–16

  25. Rosli NAIM, Ramli A (2014) Mapping bootstrap error for bilateral smoothing on point set. 21st Nat Symp Math Sci 1605(1):149–154

    Google Scholar 

  26. Rusu RB, Cousins S (2011) 3D is here: point cloud library (PCL). In: IEEE International conference on robotics and automation, pp 1–4

  27. Shi BQ, Liang J, Liu Q (2011) Adaptive simplification of point cloud using K means clustering. Comput-Aided Design 43(8):910–922

    Article  Google Scholar 

  28. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: CVPR. IEEE, pp 839–846

  29. Wand M, Berner A, Bokeloh M, Jenke P, Fleck A, Hoffmann M, Maier B, S-taneker D, Schilling A, Seidel HP (2008) Processing and interactive editing of huge point clouds from 3D scanners. Comput Graph 32(2):204–220

    Article  Google Scholar 

  30. Xu W, Lee IS, Lee SK, Lu B, Lee EJ (2015) MultiView-based hand posture recognition method based on point cloud. Ksii Trans Int Inf Sys 9(7):2585–2598

    Google Scholar 

  31. Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst

  32. Zwicker M, Pauly M (2004) Point-based computer graphics. Comput Graph 28(24):22–23

    Google Scholar 

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Authors and Affiliations

  1. Tianjin University, Tianjin, China

    Xian-Feng Han, Jesse S. Jin, Ming-Jie Wang & Wei Jiang

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  1. Xian-Feng Han
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  2. Jesse S. Jin
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  3. Ming-Jie Wang
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  4. Wei Jiang
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Correspondence to Jesse S. Jin.

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Han, XF., Jin, J.S., Wang, MJ. et al. Guided 3D point cloud filtering. Multimed Tools Appl 77, 17397–17411 (2018). https://doi.org/10.1007/s11042-017-5310-9

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  • DOI: https://doi.org/10.1007/s11042-017-5310-9

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