Abstract
For personalized design it is important to be able to collect, measure and evaluate individual properties of human beings. This paper proposes registration for the point clouds during foot 3D scanning. In the experiment, we get the point clouds of the human foot from the Artec 3D scanner and complete the registration of the point clouds from different visual angles. Dealing with the customized footwear, we choose a novel algorithm, which combines the NARF key point detector and the FPFH descriptor, to improve the efficiency of the initial iteration and reduce the computation burden of matching process.
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References
Feiyue, W.: From social computing to social manufacturing: the coming industrial revolution and new frontier in cyber-physical-social space. Bull. Chin. Acad. Sci. 6, 658–669 (2012)
Zhu, Y.J., Zhou, L.S.: Registration of scattered cloud data. J. Comput. Aided Des. Comput. Graph. 18(4), 475–487 (2006). (in Chinese)
Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Learning information point classes for the acquisition of object model maps. In: Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), Hanoi, Vietnam, 17–20 December 2008
Besl, P.J., Mckay, N.D.: Method for registration of 3-D shapes. In: Robotics: DL Tentative, pp. 239–256. International Society for Optics and Photonics (1992)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vis. 13(2), 119–152 (1994)
Radu, B.S., Rusu, R.B., Konolige, K., et al.: NARF: 3D range image features for object recognition (2010)
Steder, B., Rusu, R.B., Konolige, K., et al.: Point feature extraction on 3D range scans taking into account object boundaries, vol. 30, no. 1, pp. 2601–2608 (2011)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE Press (2009)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France, 22–26 September 2008
Lu, J., Peng, Z.T., Hong, D.L., et al.: The registration algorithm of point cloud based on optional extraction FPFH feature. J. New Ind. 7, 75–81 (2014). (in Chinese)
Price, M., Green, J., Dickens, J.: Point-cloud registration using 3D shape contexts. In: Robotics and Mechatronics Conference of South Africa (ROBOMECH), pp. 1–5 (2012)
Acknowledgment
This work was supported in part by the National Key Research and Development Program of China (No. 2018YFB1004800), the National Natural Science Foundation of China under Grants 61773381, 61773382, 61533019 and 91520301; Chinese Guangdong’s S&T Project (2016B090910001, 2017B090912001); Dongguan’s Innovation Talents Project (Gang Xiong, Jian Lu); 2017 Special Cooperative Project of Hubei Province and Chinese Academy of Sciences.
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Xie, Y. et al. (2018). The 3D Point Clouds Registration for Human Foot. In: Shi, Z., Pennartz, C., Huang, T. (eds) Intelligence Science II. ICIS 2018. IFIP Advances in Information and Communication Technology, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-01313-4_30
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DOI: https://doi.org/10.1007/978-3-030-01313-4_30
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