Multi-view point cloud registration is a hot topic in the communities of artificial intelligence and multimedia technology. In this paper, we propose a novel framework to reconstruct 3D models with a multi-view point cloud registration algorithm with adaptive convergence threshold, and apply it to 3D model retrieval subsequently. The iterative closest point (ICP) algorithm is implemented with an adaptive convergence threshold, and further combines with a motion average algorithm for the registration of multi-view point cloud data. After the registration process, the applications are designed for 3D model retrieval. The geometric saliency map is computed based on the vertex curvatures. The test facial triangles are selected to compare with the standard facial triangle. The face and non-face models are then discriminated. The experiments and comparisons demonstrate the effectiveness of the proposed framework.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Akagunduz E, Ulusoy I (2010) 3D face detection using transform invariant features. Electron Lett 46(13):905–907
Bergevin R, Soucy M, Gagnon H, et al. (1996) Towards a genral multi-view registration technique. IEEE Trans Pattern Anal Mach Intell 18(5):540–547
Boukamcha H, Elhallek M, Smach F (2015) 3D face landmark auto detection. In: World symposium on computer networks and information security, pp 1–6
Chane CS, Schutze R, Krsek P (2013) Registration of arbitrary multi-view 3D acquisitions. Comput Ind 64(9):1082–1089
Chen Y, Medioni G (1992) Object modeling by registration of multiple range images. Image Vis Comput 10(3):145–155
Chetverikov D, Stepanov D, Krsek P (2006) Robust Euclidean alignment of 3D point sets: the trimmed iterative closet point algorithm. Image Vis Comput 27 (11):1201–1208
Creusot C, Pear N, Austin J (2011) Automatic keypoint detection on 3D faces using a dictionary of local shapes. In: International conference on 3D imaging, modeling, processing, visualization and transmission, pp 204–211
Fantoni S, Castellani U, Fusiello A (2012) Accurate and automatic alignment of range surfaces. In: International conference on 3D imaging, modeling, processing, visualization and transmission, pp 73–80
Guo YL, Sohel F, Bennamoun M, et al. (2014) An accurate and robust range image registration algorithm for 3D object modeling. IEEE Trans Multimedia 16 (5):1377–1390
Guo R, Zhu JH, Li YC et al (2018) Weighted motion averaging for the registration of multi-view range scans. Multimed Tools Appl 77(1):10651–10668
Jin X, Wu Z, Song C et al (2016) 3D point cloud encryption through chaotic mapping. In: The Pacific-rim conference on multimedia, pp 119–129
Jin X, Zhu S, Xiao C et al (2017) 3D textured model encryption via 3D Lu Chaotic Mapping. Sci China Inf Sci 60(12):1–9
Lee CH, Varshney A, Jacobs DW (2005) Mesh saliency. ACM Trans Graph 24(3):659–666
Lei J, Zhou J, Mottaleb MA, et al. (2013) Detection, localization and pose classification of ear in 3D face images. In: IEEE international conference on image processing, pp 4200–4204
Li YJ, Lu HM, Kihara K et al (2017) Motor anomaly detection for aerial unmanned vehicles using temperature sensor. Artif Intell Robot 752(1):295–304
Lomonosov E, Chetverikov D, Ekart A (2006) Pre-registration of arbitrary oriented 3D surfaces using a genetic algorithm. Pattern Recogn Lett 27(11):1201–1208
Lu HM, Li YJ, Chen M (2018) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375
Rabiu H, Saripan M, Marhaban MH (2013) 3D-based face segmentation using adaptive radius. In: IEEE international conference on signal and image processing applications, pp 237–240
Sandhu R, Dambreville S, Tannenbaum A (2010) Point set registration via particle filtering and stochastic dynamics. IEEE Trans Pattern Anal Mach Intell 32 (8):1459–1473
Shi SW, Chuang YT, Yu TY (2009) An efficient and accurate method for the relaxation of multiview registration error. IEEE Trans Image Process 17(6):968–981
Wang Y, Wang F, Wang TF et al (2016) The registration of array laser point clouds based on the adaptive threshold. Acta Physica Sinica 65(24):267–277
Xu X, He L, Lu HM et al (2019) Deep adversarial metric learning for cross-modal retrieval. World Wide Web 22(2):657–672
Zhu JH, Meng DY, Li ZY et al (2014) Robust registration of partially overlapping point sets via genetic algorithm with growth operator. IET Image Process 8(10):582–590
This work was supported in part by the National Natural Science Foundation of China under Grant no. 61803298, Natural Science Foundation of Jiangsu Province under Grant no. BK20180236.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Li, Y., Liu, Y., Sun, R. et al. Multi-view point cloud registration with adaptive convergence threshold and its application in 3D model retrieval. Multimed Tools Appl 79, 14793–14810 (2020). https://doi.org/10.1007/s11042-019-7524-5
- Point cloud registration
- ICP algorithm
- Convergence threshold
- Geometric saliency
- 3D model retrieval