Improving the Depth Accuracy and Assessment of Microsoft Kinect v2 Towards a Usage for Mechanical Part Modeling

Bui Van-Bien; Banh Tien-Long; Nguyen Duc-Toan

doi:10.7736/KSPE.2019.36.8.691

Improving the Depth Accuracy and Assessment of Microsoft Kinect v2 Towards a Usage for Mechanical Part Modeling

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Type: Academic journal

Author: Bui Van-Bien (Haiphong University) Banh Tien-Long (Hanoi University of Technology and Science) Nguyen Duc-Toan (Hanoi University of Technology and Science)

Journal: Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.36 No.8 KCI Accredited Journals SCOPUS

Published: 2019.8

Pages: 691 - 697 (7page)

DOI: 10.7736/KSPE.2019.36.8.691

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Improving the Depth Accuracy and Assessment of Microsoft Kinect v2 Towards a Usage for Mechanical Part Modeling

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From 2010, the first version of Microsoft Kinect, a low-cost RGB-D camera, was released which used structured light technology to capture depth information. This device has been widely applied in many segments of the industry. In July 2014, the second version of Microsoft Kinect was launched with improved hardware. Obtaining point clouds of an observed scene with high frequency being possible leads to imaging its application to meeting the demand of 3D data acquisition. However, evaluating device capacity for mechanical part modeling has been a challenge needed to be solved. This paper intends to enhance acquired depth maps of the Microsoft Kinect v2 device for mechanical part modeling and receive an assessment about the accuracy of 3D reconstruction. Influence of materials for mechanical part modeling is also evaluated. Additionally, experimental methodology for 3D modeling of the mechanical part is finally reported to ascertain the proposed model in this paper.

#Mechanical part #3D Modeling #Point-cloud #Microsoft Kinect v2 #Depth map enhancement

1. Introduction
2. Related Works
3. Performance of Microsoft Kinect v2
4. Results and Discussion
5. Conclusions
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Khoshelham, K. and Elberink, S. O., “Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications,” Sensors, Vol. 12, No. 2, pp. 1437-1454, 2012.

Palomar, R., Cheikh, F. A., Edwin, B., Beghdadhi, A., and Elle, O. J., “Surface Reconstruction for Planning and Navigation of Liver Resections,” Computerized Medical Imaging and Graphics, Vol. 53, pp. 30-42, 2016.

Kowsari, K. and Alassaf, M. H., “Weighted Unsupervised Learning for 3D Object Detection,” International Journal of Advanced Computer Science and Applications, Vol. 7 No. 1, pp. 584-593, 2016.

Buonamici, F., Carfagni, M., Furferi, R., Governi, L., Lapini, A., et al., “Reverse Engineering of Mechanical Parts: A Template- Based Approach,” Journal of Computational Design and Engineering, Vol. 5, No. 2, pp. 145-159, 2018.

Sarbolandi, H., Lefloch, D., and Kolb, A., “Kinect Range Sensing: Structured-Light Versus Time-of-Flight Kinect,” Computer Vision and Image Understanding, Vol. 139, pp. 1-20, 2015.

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Bui Van-Bien

Affiliation Haiphong University

Fields Engineering > Mechanical Engineering > General Mechanical Engineering

Articles 1 Usage 33

Banh Tien-Long

Affiliation Hanoi University of Science and Technology

Fields Engineering > Mechanical Engineering > General Mechanical Engineering

Articles 2 Usage 71

Nguyen Duc-Toan

Affiliation Hanoi University of Science and Technology

Fields Engineering > Material/Energy Engineering > Materials Science and Engineering Engineering > Mechanical Engineering > General Mechanical Engineering

Articles 10 Usage 354

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UCI(KEPA) : I410-ECN-0101-2019-555-000936597

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