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Fast plane extraction method based on the point pair feature

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Abstract

Extracting planes from a three-dimensional (3D) point cloud is a challenging problem for many applications with 3D point clouds. In this paper, a novel fast plane extraction method based on the point pair feature (PPF) is proposed. There are two stages included in the proposed method. One is the local processing stage to sample some points in a point cloud and calculate their PPF descriptors. In this stage, the coplanar property of the PPF is used to extract initial planes from the sampling points. The other one is a global processing stage to consider all the other points in the point cloud, and assess whether they are located in the initial planes by calculating the distance from each point to the initial planes. We can extract and determine the final planes in the global processing stage. Compared with the efficient random sample consensus (RANSAC) and the 3D kernel-based Hough transform (3DKHT), the results show that for the complex scene, the extracting time of our method is less than 0.3% of the RANSAC method, and the precision rate of our method is about 9% and 17% higher than that of the RANSAC method and 3DKHT method, respectively.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Adam A, Chatzilari E, Nikolopoulos S, Kompatsiaris I (2018) H-RANSAC: a hybrid point cloud segmentation combining 2D and 3D data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4(2):1–8. https://doi.org/10.5194/isprs-annals-IV-2-1-2018

    Article  Google Scholar 

  2. Aldoma A, Marton Z-C, 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 Robotics Auto Magazine 19(3):80–91. https://doi.org/10.1109/MRA.2012.2206675

    Article  Google Scholar 

  3. Araújo AM, Oliveira MM (2020) A robust statistics approach for plane detection in unorganized point clouds. Pattern Recogn 100:107115. https://doi.org/10.1016/j.patcog.2019.107115

    Article  Google Scholar 

  4. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122. https://doi.org/10.1016/0031-3203(81)90009-1

    Article  MATH  Google Scholar 

  5. Birdal T, Busam B, Navab N, Ilic S, Sturm P (2019) Generic primitive detection in point clouds using novel minimal quadric fits. IEEE Trans Pattern Anal Mach Intell 42:1333–1347. https://doi.org/10.1109/TPAMI.2019.2900309

    Article  Google Scholar 

  6. Borrmann D, Elseberg J, Lingemann K, Nüchter A (2011) The 3d hough transform for plane detection in point clouds: a review and a new accumulator design. 3D. Research 2(2):3. https://doi.org/10.1007/3DRes.02(2011)3

    Article  Google Scholar 

  7. Bradski G, Kaehler A (2008) Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.". https://doi.org/10.1109/MRA.2009.933612

  8. Drost B, Ilic S (2015) Local hough transform for 3d primitive detection. In: 2015 international conference on 3D vision. IEEE, pp 398-406. https://doi.org/10.1109/3DV.2015.52

  9. Drost B, Ulrich M, Navab N, Ilic S (2010) Model globally, match locally: Efficient and robust 3D object recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 998–1005. https://doi.org/10.1109/cvpr.2010.5540108

  10. Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15. https://doi.org/10.1145/361237.361242

    Article  MATH  Google Scholar 

  11. El-Sayed E, Abdel-Kader RF, Nashaat H, Marei M (2018) Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction. IET Image Process 12(9):1595–1605. https://doi.org/10.1049/iet-ipr.2017.1076

    Article  Google Scholar 

  12. Fan W, Shi W, Xiang H, Ding K (2019) A novel method for plane extraction from low-resolution inhomogeneous point clouds and its application to a customized low-cost Mobile mapping system. Remote Sens 11(23). https://doi.org/10.3390/rs11232789

  13. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395. https://doi.org/10.1145/358669.358692

    Article  MathSciNet  Google Scholar 

  14. Guinard SA, Mallé Z, Ennafii O, Monasse P, Vallet B (2020) Planar polygons detection in lidar scans based on sensor topology enhanced RANSAC. ISPRS Ann Photogrammetry, Remote Sensing Spatial Inform Sci 5(2). https://doi.org/10.5194/isprs-annals-V-2-2020-343-2020

  15. Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2020) Deep learning for 3d point clouds: a survey. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.3005434

  16. Holz D, Behnke S (2013) Fast range image segmentation and smoothing using approximate surface reconstruction and region growing. Intell Auto Syst 12:61–73. https://doi.org/10.1007/978-3-642-33932-5_7

    Article  Google Scholar 

  17. Hu L, Xiao J, Wang Y (2020) Efficient and automatic plane detection approach for 3-D rock mass point clouds. Multimed Tools Appl 79(1–2):839–864. https://doi.org/10.1007/s11042-019-08189-6

    Article  Google Scholar 

  18. Jiang P, Ishihara Y, Sugiyama N, Oaki J, Tokura S, Sugahara A, Ogawa A (2020) Depth image–based deep learning of grasp planning for textureless planar-faced objects in vision-guided robotic bin-picking. Sensors 20(3):706. https://doi.org/10.3390/s20030706

    Article  Google Scholar 

  19. Jin Z, Tillo T, Zou W, Zhao Y, Li X (2017) Robust plane detection using depth information from a consumer depth camera. IEEE Trans Circuits Syst Video Technol 29(2):447–460. https://doi.org/10.1109/TCSVT.2017.2780181

    Article  Google Scholar 

  20. Kaiser A, Ybanez Zepeda JA, Boubekeur T (2019) A survey of simple geometric primitives detection methods for captured 3d data. In: Computer Graphics Forum, vol 1. Wiley Online Library, pp 167–196. https://doi.org/10.1111/cgf.13451

  21. Li Y, Wu X, Chrysathou Y, Sharf A, Cohen-Or D, Mitra NJ (2011) Globfit: consistently fitting primitives by discovering global relations. In: ACM transactions on graphics (TOG), vol 4. ACM, p 52. https://doi.org/10.1145/1964921.1964947

  22. Li L, Yang F, Zhu H, Li D, Li Y, Tang L (2017) An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells. Remote Sens 9(5):433. https://doi.org/10.3390/rs9050433

    Article  Google Scholar 

  23. Limberger FA, Oliveira MM (2015) Real-time detection of planar regions in unorganized point clouds. Pattern Recogn 48(6):2043–2053. https://doi.org/10.1016/j.patcog.2014.12.020

    Article  Google Scholar 

  24. Rao G, Wang G, Yang X, Xu J, Chen K (2018) Normal direction measurement and optimization with a dense three-dimensional point cloud in robotic drilling. IEEE/ASME Trans Mechatron 23(3):986–996. https://doi.org/10.1109/TMECH.2017.2747133

    Article  Google Scholar 

  25. Rusu RB, Cousins S (2011) 3d is here: point cloud library (pcl). In: 2011 IEEE international conference on robotics and automation. IEEE, pp 1-4. https://doi.org/10.1109/ICRA.2011.5980567

  26. Sarker IH, Abushark YB, Alsolami F, Khan AI (2020) Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry 12(5):754. https://doi.org/10.3390/sym12050754

    Article  Google Scholar 

  27. Sarker IH, Khan AI, Abushark YB, Alsolami F (2022) Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mobile Networks Appl:1–17. https://doi.org/10.1007/s11036-022-01937-3

  28. Sayed A, Ibrahim A (2018) Recent developments in systematic sampling: a review. J Statis Theory Pract 12(2):290–310. https://doi.org/10.1080/15598608.2017.1353456

    Article  MathSciNet  Google Scholar 

  29. Schnabel R, Wahl R, Klein R (2007) Efficient RANSAC for point-cloud shape detection. Comput Graphics Forum 26(2):214–226. https://doi.org/10.1111/j.1467-8659.2007.01016.x

    Article  Google Scholar 

  30. Tian Y, Song W, Chen L, Sung Y, Kwak J, Sun S (2020) Fast planar detection system using a GPU-based 3D Hough transform for LiDAR point clouds. Appl Sci 10(5):1744. https://doi.org/10.3390/app10051744

    Article  Google Scholar 

  31. VC HP (1962) Method and means for recognizing complex patterns. US Patent 3,069,654

  32. Vera E, Lucio D, Fernandes LA, Velho L (2018) Hough transform for real-time plane detection in depth images. Pattern Recogn Lett 103:8–15. https://doi.org/10.1016/j.patrec.2017.12.027

    Article  Google Scholar 

  33. Verma R, Verma AK (2020) An efficient clustering algorithm to simultaneously detect multiple Planes in a point cloud. In: 2020 3rd international conference on emerging Technologies in Computer Engineering: machine learning and internet of things (ICETCE). IEEE, pp 154-159. https://doi.org/10.1109/ICETCE48199.2020.9091735

  34. Wahl E, Hillenbrand U, Hirzinger G (2003) Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification. In: fourth international conference on 3-D digital imaging and modeling. IEEE, pp 474-481. https://doi.org/10.1109/IM.2003.1240284

  35. Xiao Z, Gao J, Wu D, Zhang L, Chen X (2020) A fast 3D object recognition algorithm using plane-constrained point pair features. Multimed Tools Appl 79(39):29305–29325. https://doi.org/10.1007/s11042-020-09525-x

    Article  Google Scholar 

  36. Xu S, Wang RS, Wang H, Yang RG (2021) Plane segmentation based on the optimal-vector-field in LiDAR point clouds. IEEE Trans Pattern Anal Mach Intell 43(11):3991–4007. https://doi.org/10.1109/tpami.2020.2994935

    Article  Google Scholar 

  37. Yang LN, Li YC, Li XC, Meng ZQ, Luo HW (2022) Efficient plane extraction using normal estimation and RANSAC from 3D point cloud. Comput Stand Interfaces 82:14. https://doi.org/10.1016/j.csi.2021.103608

    Article  Google Scholar 

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Acknowledgments

This research was funded by the National Natural Science Foundation of China under Grant No. 52075106. We gratefully thank the anonymous reviewers for their comments and suggestions for improving this paper.

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Authors and Affiliations

  1. School of Electromechanical Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou, 510510, China

    Zhengtao Xiao

  2. State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, 510006, China

    Jian Gao, Lanyu Zhang & Qiaofen Zhang

  3. School of Computational Science, Zhongkai University of Agriculture and Engineering, Guangzhou, 510220, China

    Dongqing Wu

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  1. Zhengtao Xiao
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Correspondence to Jian Gao.

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Xiao, Z., Gao, J., Wu, D. et al. Fast plane extraction method based on the point pair feature. Multimed Tools Appl 82, 15027–15042 (2023). https://doi.org/10.1007/s11042-022-14063-9

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