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Offline In-Hand 3D Modeling System Using Automatic Hand Removal and Improved Registration Method  

Kang, Junseok (과학기술연합대학원대학교 KIST 스쿨 나노-정보 융합 (HCI 및 로봇공학))
Yang, Hyeonseok (과학기술연합대학원대학교 KIST 스쿨 나노-정보 융합 (HCI 및 로봇공학))
Lim, Hwasup (과학기술연합대학원대학교 나노-정보 융합, KIST 영상미디어연구단)
Ahn, Sang Chul (과학기술연합대학원대학교 나노-정보 융합, KIST 영상미디어연구단)
Publication Information
Journal of the HCI Society of Korea / v.12, no.3, 2017 , pp. 13-23 More about this Journal
Abstract
In this paper, we propose a new in-hand 3D modeling system that improves user convenience. Since traditional modeling systems are inconvenient to use, an in-hand modeling system has been studied, where an object is handled by hand. However, there is also a problem that it requires additional equipment or specific constraints to remove hands for good modeling. In this paper, we propose a contact state change detection algorithm for automatic hand removal and improved ICP algorithm that enables outlier handling and additionally uses color for accurate registration. The proposed algorithm enables accurate modeling without additional equipment or any constraints. Through experiments using real data, we show that it is possible to accomplish accurate modeling under the general conditions without any constraint by using the proposed system.
Keywords
In-hand modeling; Contact state change detection; Tracking; Registration; Outlier handling;
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1 Park, S. and Murali, S. A multiview 3D modeling system based on stereo vision techniques. Machine Vision and Applications. 16(3). pp. 148-156. 2005.   DOI
2 Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J. and Fulk, D. The digital Michelangelo project: 3D scanning of large statues. Proceedings of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co. pp. 131-144. 2000.
3 Beraldin, J. A. Integration of laser scanning and close-range photogrammetry-The last decade and beyond. Proceedings of the XXth ISPRS Congress. 35(B). pp. 972-983. 2004.
4 Sense 3D scanner. https://3dsystems.com/shop/sense/. 2016. 12. 22
5 EORA 3D. https://eora3d.com/product.html. 2016.12.22
6 Weise, T., Leibe, B. and Van Gool, L. Accurate and robust registration for in-hand modeling. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference. pp. 1-8. 2008.
7 Weise, T., Wismer, T., Leibe, B. and Van Gool, L. In-hand scanning with online loop closure. Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference. pp. 1630-1637. 2009.
8 Saelzle, M. In-hand scanner for small objects. http://pointclouds.org/documentation/tutorials/in_hand_scanner.php. 2017. 1. 17.
9 Panteleris, P., Kyriazis, N. and Argyros, A. A. 3D Tracking of Human Hands in Interaction with Unknown Objects. British Machine Vision Conference (BMVC). pp. 123.1-123.12. 2015.
10 Jones, M. J. and Rehg, J. M. Statistical color models with application to skin detection. International Journal of Computer Vision. 46(1). pp. 81-96. 2002.   DOI
11 Oikonomidis, I., Kyriazis, N. and Argyros, A. A. Efficient model-based 3D tracking of hand articulations using Kinect. British Machine Vision Conference (BMVC). 1(2). pp. 1-11. 2011.
12 Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R. and Wu, A. Y. An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence. 24(7). pp. 881-892. 2002.   DOI
13 Xue, H., Chen, S. and Yang, Q. Structural support vector machine. Advances in Neural Networks-ISNN 2008. pp. 501-511. 2008.
14 Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M. M., Hicks, S. L. and Torr, P. H. Struck: Structured output tracking with kernels.IEEE transactions on pattern analysis and machine intelligence. 38(10). pp. 2096-2109. 2016.   DOI
15 Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohli, P., Shotton, J., Hodges, S. and Fitzgibbon, A. KinectFusion: Real-time dense surface mapping and tracking. Mixed and augmented reality (ISMAR), 2011 10th IEEE international symposium. pp. 127-136. 2011.
16 Klingensmith, M., Dryanovski, I., Srinivasa, S. and Xiao, J. Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields. Robotics: Science and Systems. 2015.
17 Kazhdan, M. and Hoppe, H. Screened poisson surface reconstruction. ACM Transactions on Graphics (TOG). 32(3). Article No. 29. 2013.
18 Besl, P. J. and McKay, N. D. Method for registration of 3-D shapes. Robotics-DL tentative. International Society for Optics and Photonics. 14(2). pp. 239-256. 1992.
19 Chen, Y. and Medioni, G. Object modelling by registration of multiple range images. Image and vision computing. 10(3). pp. 145-155. .1992.   DOI
20 Zhou, Q. Y., Park, J. and Koltun, V. Fast global registration. European Conference on Computer Vision. Springer International Publishing. pp. 766-782. 2016.
21 Garland, M. and Heckbert, P. S. Surface simplification using quadric error metrics. Proceedings of the 24th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co. pp. 209-216. 1997.
22 Waechter, M., Moehrle, N. and Goesele, M. Let there be color! Large-scale texturing of 3D reconstructions. European Conference on Computer Vision. SpringerInternational Publishing. pp. 836-850. 2014.