基于多类型几何图元匹配的三维点云配准

doi:10.7523/j.ucas.2021.0047

摘要/Abstract

摘要： 三维点云配准是计算机视觉、计算机图形学和遥感领域内诸多应用的重要基础，然而待配准数据中常有的噪声和共有区域小等问题给点云配准带来了极大的挑战。针对可能具有以上问题的人造物体点云或城市场景点云，提出一个基于多类型几何图元匹配的点云配准方法。该方法首先在原始点云中进行图元提取并基于它们的有效组合创建特征描述子，然后在描述子匹配的基础上，实现图元匹配并从中计算出变换参数，最后利用全局评估策略选出最佳变换参数并以此实现点云配准。该方法充分继承了多类型图元的优势，具有更强的鲁棒性和高效性，实验结果表明该方法在多种基准数据上取得了最佳的配准效果。

关键词: 三维点云, 点云配准, 几何图元, 有效组合

Abstract: 3D point cloud registration is the fundamental of a large number of applications in computer vision, computer graphics, and remote sensing, etc. However, the existence of noises and a low overlapping ratio in the scanning data poses a great challenge to the existing registration methods. Facing the point clouds of man-made objects or urban scenes that are likely to have the aforementioned issues, we propose a registration method of 3D point clouds via matching multi types of geometric primitives. Our method first extracts common geometric primitives from raw point clouds and further builds the feature descriptors from their effective combinations. Then, under the matching of the descriptors, our method realizes the matching of primitives and acquires the transformation parameters from them. Finally, based on the global evaluation for every candidate transformation, the best transformation is identified and applied to achieve the registration. Our method fully inherits the advantages of multiple types of primitives and has stronger robustness and efficiency. Experiments on various benchmarks demonstrate that our method achieves state-of-the-art registration performance.

Key words: 3D point cloud, point cloud registration, geometric primitive, effective combination

中图分类号:

TP391.41

张龙, 肖俊, 程晓龙, 王颖. 基于多类型几何图元匹配的三维点云配准[J]. 中国科学院大学学报, 2023, 40(2): 258-267.

ZHANG Long, XIAO Jun, CHENG Xiaolong, WANG Ying. 3D point cloud registration via matching multi types of geometric primitives[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(2): 258-267.

参考文献

[1] Tam G K L, Cheng Z Q, Lai Y K, et al. Registration of 3D point clouds and meshes:a survey from rigid to nonrigid[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(7):1199-1217.
[2] Maiseli B, Gu Y F, Gao H J. Recent developments and trends in point set registration methods[J]. Journal of Visual Communication and Image Representation, 2017, 46:95-106.
[3] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256.
[4] Chen Y, Medioni G. Object modelling by registration of multiple range images[J]. Image and Vision Computing, 1992, 10(3):145-155.
[5] Rueckert D, Sonoda L I, Hayes C, et al. Nonrigid registration using free-form deformations:application to breast MR images[J]. IEEE Transactions on Medical Imaging, 1999, 18(8):712-721.
[6] Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm[C]//Proceedings Third International Conference on 3-D Digital Imaging and Modeling. May 28-June 1, 2001, Quebec City, QC, Canada. IEEE, 2001:145-152.
[7] Li H, Sumner R W, Pauly M. Global correspondence optimization for non-rigid registration of depth scans[J]. Computer Graphics Forum, 2008, 27(5):1421-1430.
[8] Bouaziz S, Tagliasacchi A, Pauly M. Sparse iterative closest point[J]. Computer Graphics Forum, 2013, 32(5):113-123.
[9] 王飞鹏, 肖俊, 王颖, 等. 一种基于高斯曲率的ICP改进算法[J]. 中国科学院大学学报, 2019, 36(5):702-708.
[10] Xiao J H, Adler B, Zhang H X. 3D point cloud registration based on planar surfaces[C]//2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). September 13-15, 2012, Hamburg, Germany. IEEE, 2012:40-45.
[11] Xu Y, Boerner R, Yao W, et al. Automated coarse registration of point clouds in 3d urban scenes using voxel based plane constraint[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, IV-2/W4:185-191.
[12] Chen S L, Nan L L, Xia R B, et al. PLADE:a plane-based descriptor for point cloud registration with small overlap[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4):2530-2540.
[13] Zhong Y. Intrinsic shape signatures:a shape descriptor for 3D object recognition[C]//2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. September 27-October 4, 2009, Kyoto, Japan. IEEE, 2009:689-696.
[14] Sipiran I, Bustos B. Harris 3D:a robust extension of the Harris operator for interest point detection on 3D meshes[J]. The Visual Computer, 2011, 27(11):963-976.
[15] Guo Y L, Bennamoun M, Sohel F, et al. A comprehensive performance evaluation of 3D local feature descriptors[J]. International Journal of Computer Vision, 2016, 116(1):66-89.
[16] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//2009 IEEE International Conference on Robotics and Automation. May 12-17, 2009, Kobe, Japan. IEEE, 2009:3212-3217.
[17] Tombari F, Salti S, Stefano L. Unique signatures of histograms for local surface description[C]//Computer Vision-ECCV 2010, 2010:356-369. DOI:10.1007/978-3-642-15558-1_26.
[18] Zhou Q Y, Park J, Koltun V. Fast global registration[M]//Computer Vision-ECCV 2016. Cham:Springer International Publishing, 2016:766-782.
[19] Wolfson H J, Rigoutsos I. Geometric hashing:an overview[J]. IEEE Computational Science and Engineering, 1997, 4(4):10-21.
[20] Fischler M A, Bolles R C. Random sample consensus[J]. Communications of the ACM, 1981, 24(6):381-395.
[21] Chen C S, Hung Y P, Cheng J B. RANSAC-based DARCES:A new approach to fast automatic registration of partially overlapping range images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(11):1229-1234.
[22] Aiger D, Mitra N J, Cohen-Or D. 4-points congruent sets for robust pairwise surface registration[C]//ACM SIGGRAPH 2008 papers on-SIGGRAPH'08. August 11-15, 2008. Los Angeles, California. New York:ACM Press, 2008:1-10.
[23] Mellado N, Aiger D, Mitra N J. Super 4PCS fast global pointcloud registration via smart indexing[J]. Computer Graphics Forum, 2014, 33(5):205-215.
[24] Habib A, Ghanma M, Morgan M, et al. Photogrammetric and lidar data registration using linear features[J]. Photogrammetric Engineering & Remote Sensing, 2005, 71(6):699-707.
[25] Al-Durgham M, Habib A. A procedure for the registration and segmentation of heterogeneous lidar data[C]//2012 International Conference on Computer Vision in Remote Sensing. December 16-18, 2012, Xiamen, China. IEEE, 2012:122-126.
[26] Yang B S, Zang Y F. Automated registration of dense terrestrial laser-scanning point clouds using curves[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 95:109-121.
[27] Xu Y S, Boerner R, Yao W, et al. Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151:106-123.
[28] Hattab A, Taubin G. 3D rigid registration of cad point-clouds[C]//2018 International Conference on Computing Sciences and Engineering (ICCSE). March 11-13, 2018, Kuwait, Kuwait. IEEE, 2018:1-6.
[29] Charles R Q, Hao S, Mo K C, et al. PointNet:deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017:77-85.
[30] Qi C R, Yi L, Su H, et al. PointNet++:deep hierarchical feature learning on point sets in a metric space[EB/OL]. arXiv:1706.02413. (2017-06-07)[2021-03-04]. https://arxiv.org/abs/1706.02413.
[31] Gojcic Z, Zhou C F, Wegner J D, et al. The perfect match:3D point cloud matching with smoothed densities[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA. IEEE, 2019:5540-5549.
[32] Zeng A, Song S R, Nießner M, et al. 3DMatch:learning local geometric descriptors from RGB-D reconstructions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017:199-208.
[33] Aoki Y, Goforth H, Srivatsan R A, et al. PointNetLK:robust & efficient point cloud registration using PointNet[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA. IEEE, 2019:7156-7165.
[34] Wang Y, Solomon J. Deep closest point:learning representations for point cloud registration[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019, Seoul, Korea (South). IEEE, 2019:3522-3531.
[35] Wu Z R, Song S R, Khosla A, et al. 3D ShapeNets:a deep representation for volumetric shapes[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 7-12, 2015, Boston, MA, USA. IEEE, 2015:1912-1920.
[36] Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 2007, 26(2):214-226.
[37] Hackel T, Savinov N, Ladicky L, et al. semantic3d.net:a new large-scale point cloud classification benchmark[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, IV-1/W1:91-98.
[38] Lai K, Bo L F, Fox D. Unsupervised feature learning for 3D scene labeling[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). May 31-June 7, 2014, Hong Kong, China. IEEE, 2014:3050-3057.
[39] Dong Z, Yang B S, Liang F X, et al. Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144:61-79.