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Proceedings Paper

Calibration of multiple Kinect depth sensors for full surface model reconstruction
Author(s): Kwan Pang Tsui; Kin Hong Wong; Changling Wang; Ho Chuen Kam; Hing Tuen Yau; Ying Kin Yu
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Paper Abstract

In this paper, we have investigated different methods to calibrate a 3-D scanning system consisting of multiple Kinect sensors. The main function of the scanning system is for the reconstruction of the full surface model of an object. In this work, we build a four-Kinect system that the Kinect range sensors are positioned around the target object. Each Kinect is responsible for capturing a small local model, and the local models found will be combined to become the full model. To build such a system, calibration of the poses among the Kinects is essential. We have tested a number of methods: using (1) a sphere, (2) a checker board and (3) a cube as the calibration object. After calibration, the results of method (1) and (2) are used in the multiple Kinect system for obtaining the 3-D model of a real object. Results are shown and compared. For method (3) we only performed the simulation test on finding the rotation between two Kinects and the result is promising. This is the first part of a long term project on building a full surface model capturing system. Such a system should be useful in robot vision, scientific research and many other industrial applications.

Paper Details

Date Published: 11 July 2016
PDF: 6 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111H (11 July 2016); doi: 10.1117/12.2241159
Show Author Affiliations
Kwan Pang Tsui, The Chinese Univ. of Hong Kong (Hong Kong, China)
Kin Hong Wong, The Chinese Univ. of Hong Kong (Hong Kong, China)
Changling Wang, The Chinese Univ. of Hong Kong (Hong Kong, China)
Ho Chuen Kam, The Chinese Univ. of Hong Kong (Hong Kong, China)
Hing Tuen Yau, The Chinese Univ. of Hong Kong (Hong Kong, China)
Ying Kin Yu, The Chinese Univ. of Hong Kong (Hong Kong, China)


Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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