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Complete End-to-end Low Cost Solution to a 3D Scanning System with Integrated Turntable


Affiliations
1 Erasmus Joint Master Program in Medical Imaging and Applications, University of Burgundy, France
2 Erasmus Joint Master Program in Medical Imaging and Applications, University of Cassino, Italy
3 Erasmus Joint Master Program in Medical Imaging and Applications, University of Girona, Spain
 

3D reconstruction is a technique used in computer vision and it has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardware was required. Such system was often very expensive and was only available for industrial or research purpose. Nowadays, with the rise of high-quality 3D scanners available at low price, it is possible to design complete 3D scanning systems at very low cost. The objective of this work is to design a homemade acquisition and processing system to perform 3D scanning and reconstruction of objects. The goal of this work also includes making the 3D scanning process fully automated by building and integrating a turntable alongside the software. İn addition, the user is able to perform a full 3D scan by the press of a few buttons on our dedicated Graphical User Interface (GUI) which has been designed for this purpose. Hence, the product of our work will be an acquisition and a processing software capable of controlling the turning table, acquire point cloud frames, register them and reconstruct the 3D mesh which can be exported afterwards to a 3D printer. To achieve this goal, three main steps were required. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.

Keywords

3D Body Scanning, 3D Printing, 3D Reconstruction, Iterative Closest Process, Automated Scanning System, Kinect v2.0 Sensor, RGB-D Camera, Point Cloud Library (PCL).
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  • David Kirk, Abigail Sellen, Stuart Taylor, Nicolas Villar, and Shahram Izadi. Putting the physical into the digital: issues in designing hybrid interactive surfaces. In Proceedings of the 23rd British.
  • Jürgen Sturm, Erik Bylow, Fredrik Kahl, and Daniel Cremers. Copyme3d: Scanning and printing persons in 3d. In German Conference on Pattern Recognition, pages 405–414. Springer, 2013.
  • Tong, Jing, et al. "Scanning 3d full human bodies using kinects." IEEE transactions on visualization and computer graphics 18.4 (2012): 643-650.
  • Dou, Mingsong, et al. "3d scanning deformable objects with a single rgbd sensor." 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015.
  • Khoshelham, Kourosh, and Sander Oude Elberink. "Accuracy and resolution of kinect depth data for indoor mapping applications." Sensors 12.2 (2012): 1437-1454.
  • Weiss, Alexander, David Hirshberg, and Michael J. Black. "Home 3D body scans from noisy image and range data." 2011 International Conference on Computer Vision. IEEE, 2011.
  • Song Tiangang, Lyu Zhou, Ding Xinyang, andWan Yi. 3d surface reconstruction based on kinect sensor. International Journal of Computer Theory and Engineering, 5(3):567, 2013.
  • HGonzalez-Jorge, B Riveiro, E Vazquez-Fernandez, JMartínez-Sánchez, and P Arias. Metrological evaluation of microsoft kinect and asus xtion sensors. Measurement, 46(6):1800–1806, 2013.
  • Richard Szeliski. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
  • Szymon Rusinkiewicz and Marc Levoy. Efficient variants of the icp algorithm. In 3-D Digital Imaging and Modeling, 2001. Proceedings. Third International Conference on, pages 145– 152. IEEE, 2001.
  • Niloy JMitra, Natasha Gelfand, Helmut Pottmann, and Leonidas Guibas. Registration of point cloud data froma geometric optimization perspective. In Proceedings of the 2004 Eurographics/ ACM SIGGRAPH symposium on Geometry processing, pages 22–31. ACM, 2004.
  • Lingni Ma, Thomas Whelan, Egor Bondarev, Peter HN de With, and John McDonald. Planar simplification and texturing of dense point cloudmaps. InMobile Robots (ECMR), 2013 European Conference on, pages 164–171. IEEE, 2013.
  • Matthew T Dickerson, Robert L Scot Drysdale, Scott AMcElfresh, and EmoWelzl. Fast greedy triangulation algorithms. In Proceedings of the tenth annual symposium on Computational geometry, pages 211–220. ACM, 1994.
  • Juha Hyvärinen et al. Surface reconstruction of point clouds captured with microsoft kinect. 2012.
  • Ruosi Li, Lu Liu, Ly Phan, Sasakthi Abeysinghe, Cindy Grimm, and Tao Ju. Polygonizing extremal surfaces with manifold guarantees. In Proceedings of the 14th ACM Symposium on Solid and PhysicalModeling, pages 189–194. ACM, 2010.
  • Thuong Le-Tien,Marie Luong, Thai Phu Ho, and Viet Dai Tran. 3d reconstruction using kinectsensor and parallel processing on 3d graphics processing unit. REV Journal on Electronics and Communications, 3(1-2), 2013.
  • Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposiumon Geometry processing, volume 7, 2006.
  • Michael Kazhdan and Hugues Hoppe. Screened poisson surface reconstruction. ACMTransactions on Graphics (TOG), 32(3):29, 2013.
  • Radu Bogdan Rusu and Steve Cousins. 3D is here: Point Cloud Library (PCL). In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China,May 9-13 2011.
  • Myronenko, Andriy, and Xubo Song. "Point set registration: Coherent point drift." IEEE transactions on pattern analysis and machine intelligence 32.12 (2010): 2262-2275.
  • Smoothing and normal estimation based on polynomial reconstruction, 2017 http://pointclouds.org/documentation/tutorials/resampling.php
  • Callahan, Michael, et al. Biomesh3d: A meshing pipeline for biomedical computing. Technical report, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112 USA.

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  • Complete End-to-end Low Cost Solution to a 3D Scanning System with Integrated Turntable

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Authors

Saed Khawaldeh
Erasmus Joint Master Program in Medical Imaging and Applications, University of Burgundy, France
Tajwar Abrar Aleef
Erasmus Joint Master Program in Medical Imaging and Applications, University of Cassino, Italy
Usama Pervaiz
Erasmus Joint Master Program in Medical Imaging and Applications, University of Girona, Spain
Vu Hoang Minh
Erasmus Joint Master Program in Medical Imaging and Applications, University of Burgundy, France
Andyeman Brhane Hagos
Erasmus Joint Master Program in Medical Imaging and Applications, University of Burgundy, France

Abstract


3D reconstruction is a technique used in computer vision and it has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardware was required. Such system was often very expensive and was only available for industrial or research purpose. Nowadays, with the rise of high-quality 3D scanners available at low price, it is possible to design complete 3D scanning systems at very low cost. The objective of this work is to design a homemade acquisition and processing system to perform 3D scanning and reconstruction of objects. The goal of this work also includes making the 3D scanning process fully automated by building and integrating a turntable alongside the software. İn addition, the user is able to perform a full 3D scan by the press of a few buttons on our dedicated Graphical User Interface (GUI) which has been designed for this purpose. Hence, the product of our work will be an acquisition and a processing software capable of controlling the turning table, acquire point cloud frames, register them and reconstruct the 3D mesh which can be exported afterwards to a 3D printer. To achieve this goal, three main steps were required. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.

Keywords


3D Body Scanning, 3D Printing, 3D Reconstruction, Iterative Closest Process, Automated Scanning System, Kinect v2.0 Sensor, RGB-D Camera, Point Cloud Library (PCL).

References