Abstract
To deal with data sets from real-time 3D sensors of RGB-D or TOF cameras, this paper presents a method for registration of unstructured point clouds. We firstly derive intrinsic shape context descriptors for 3D data organization. To replace the Fast-Marching method, a vertex-oriented triangle propagation method is applied to calculate the ’angle’ and ’radius’ in descriptor charting, so that the matching accuracy at the twisting and folding area is significantly improved. Then, a 3D cylindrical shape descriptor is proposed for registration of unstructured point clouds. The chosen points are projected into the cylindrical coordinate system to construct the descriptors. The projection parameters are respectively determined by the distances from the chosen points to the reference normal vector, and the distances from the chosen points to the reference tangent plane and the projection angle. Furthermore, Fourier transform is adopted to deal with orientation ambiguity in descriptor matching. Practical experiments demonstrate a satisfactory result in point cloud registration and notable improvement on standard benchmarks.
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References
Agarwal, S., Bhowmick, B.: 3d point cloud registration with shape constraint. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2199–2203 (2017)
Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: 2011 IEEE international conference on computer vision workshops (ICCV workshops), pp. 1626–1633 (2011)
Bogo, F., Romero, J., Loper, M., Black, M.J.: Faust: dataset and evaluation for 3d mesh registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3794–3801 (2014)
Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1704–1711 (2010)
Chen, Y., Medioni, G.G.: Object modeling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)
Elad, A., Kimmel, R.: On bending invariant signatures for surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1285–1295 (2003)
Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: European conference on computer vision, pp. 224–237. Springer, New York (2004)
Gozdzik, M., Slot, K.: Object representation using geodesic levels. In: 2007 International Symposium on Information Technology Convergence (ISITC 2007), pp. 222–226. IEEE (2007)
Grundmann, M., Meier, F., Essa, I.: 3d shape context and distance transform for action recognition. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
He, Y., Chen, S.: Advances in sensing and processing methods for three-dimensional robot vision. Int. J. Adv. Rob. Syst. 15(2), 1729881418760623 (2018)
He, Y., Chen, S.: Recent advances in 3d data acquisition and processing by time-of-flight camera. IEEE Access 7, 12495–12510 (2019)
Hu, W., Qu, Y.: 3d reconstruction from multi-view point cloud based on particle system. In: 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009), pp. 500–503. IEEE (2009)
Jiao, Z., Liu, R., Yi, P., Zhou, D.: A point cloud registration algorithm based on 3d-sift. In: Transactions on Edutainment XV, pp. 24–31. Springer, New York (2019)
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)
Kleppe, A.L., Egeland, O.: A curvature-based descriptor for point cloud alignment using conformal geometric algebra. Adv. Appl. Clifford Algebras 28(2), 50 (2018)
Kokkinos, I., Bronstein, M.M., Litman, R., Bronstein, A.M.: Intrinsic shape context descriptors for deformable shapes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 159–166. IEEE (2012)
Qi, C.R., Su, H., Mo, K., 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, pp. 652–660 (2017)
Qin, Y., Han, X., Yu, H., Yu, Y., Zhang, J.: Fast and exact discrete geodesic computation based on triangle-oriented wavefront propagation. ACM Trans. Graph. (TOG) 35(4), 1–13 (2016)
Shi, Y., Thompson, P.M., de Zubicaray, G.I., Rose, S.E., Tu, Z., Dinov, I., Toga, A.W.: Direct mapping of hippocampal surfaces with intrinsic shape context. NeuroImage 37(3), 792–807 (2007)
Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Computer graphics forum, vol. 28, pp. 1383–1392. Wiley Online Library (2009)
Wang, G., Wang, Y.: Multi-scale heat kernel based volumetric morphology signature. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 751–759, Springer, New York (2015)
Wang, Z., Liu, S., Zhang, J., Chen, S., Guan, Q.: A spatio-temporal crf for human interaction understanding. IEEE Trans. Circ. Syst. Video Technol. 27(8), 1647–1660 (2017)
Yang, J., Cao, Z., Zhang, Q.: A fast and robust local descriptor for 3d point cloud registration. Inf. Sci. 346, 163–179 (2016)
Zhang, J., Chen, S., Liu, S., Guan, Q.: Normalized weighted shape context and its application in feature-based matching. Opt. Eng. 47(9), 097201 (2008)
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19(2), 4–10 (2012)
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This work was supported by National Key R&D Program of China (2018YFB1305200), National Natural Science Foundation of China (62020106004) and EUH2020 RISE project-AniAge (691215).
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He, Y., Chen, S., Yu, H. et al. A cylindrical shape descriptor for registration of unstructured point clouds from real-time 3D sensors. J Real-Time Image Proc 18, 261–269 (2021). https://doi.org/10.1007/s11554-020-01033-3
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DOI: https://doi.org/10.1007/s11554-020-01033-3