Calibration of multiple Kinect depth sensors for full surface model reconstruction

Kwan Pang Tsui; Kin Hong Wong; Changling Wang; Ho Chuen Kam; Hing Tuen Yau; Ying Kin Yu

doi:10.1117/12.2241159

11 July 2016 Calibration of multiple Kinect depth sensors for full surface model reconstruction

Kwan Pang Tsui, Kin Hong Wong, Changling Wang, Ho Chuen Kam, Hing Tuen Yau, Ying Kin Yu

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100111H (2016) https://doi.org/10.1117/12.2241159
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

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.

Citation Download Citation

Kwan Pang Tsui, Kin Hong Wong, Changling Wang, Ho Chuen Kam, Hing Tuen Yau, and Ying Kin Yu "Calibration of multiple Kinect depth sensors for full surface model reconstruction", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111H (11 July 2016); https://doi.org/10.1117/12.2241159

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