Calibration of multi-Kinect and multi-camera setup for full 3D reconstruction

Kim, Jae-Hean; Choi, Jin Sung; Koo, Bon-Ki

doi:10.1109/isr.2013.6695729

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…Different from the methods to calibrate depths by using disparity images, other researchers believed that the error of depth measurement was caused by the factor of distortion from both an infrared emitter and an infrared camera [45]. Based on this point, the depth could be corrected by calibrating the distortion component [45] or the relative pose [46] between the infrared emitter and infrared camera, respectively. Combining this method with the research studies on the depth correction in [45,46], the researchers [47,48] calibrated the depth measurements by optimizing the distortion and relative pose between an infrared emitter and infrared camera simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Based on this point, the depth could be corrected by calibrating the distortion component [45] or the relative pose [46] between the infrared emitter and infrared camera, respectively. Combining this method with the research studies on the depth correction in [45,46], the researchers [47,48] calibrated the depth measurements by optimizing the distortion and relative pose between an infrared emitter and infrared camera simultaneously. However, the methods could only calibrate the system error existing in a depth camera.…”

Section: Introductionmentioning

confidence: 99%

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A New Model of RGB-D Camera Calibration Based on 3D Control Field

Zhang

Huang

Zhao

2019

Sensors

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With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Model of RGB-D Camera Calibration Based on 3D Control Field

Zhang

Huang

Zhao

2019

Sensors

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“…To enhance the reliability of the data acquired from the depth camera and to obtain various complicated gestures, a number of studies have been carried out on three-dimensional (3D) expression and data correction methods. The observations of these studies have resulted in the accurate recognition of user gestures and more complicated gestures in diverse environments [ 7 , 8 ]. In recent years, the domain of gesture recognition-related studies has been expanded to include virtual reality (which is currently attracting significant interest) and to smart homes based on context awareness.…”

Section: Introductionmentioning

confidence: 99%

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition

Choi

Kim

2017

Sensors

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Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user’s location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

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“…Next, for accurate calibration, which considers not only the errors of color images but also those of range data, we optimize the estimated parameters by minimizing both the errors of correspondences between color images and those of range data of planar regions, which exist in a general environment such as walls and floors. Although our method is composed of well-known processes, we properly select and combine them with the consideration of the interference problem, which existing methods for calibrating extrinsic parameters of multiple Kinects [10][11][12][13][14] do not consider. regarded that they have the same intrinsic and extrinsic parameters through alignment between the range data and the color image, which is provided by the Kinect SDK function with calibration information on factory default configuration.…”

Section: Introductionmentioning

confidence: 99%

Calibration of multiple kinects with little overlap regions

Nakazawa

Mitsugami

Habe

et al. 2015

IEEJ Transactions Elec Engng

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When using multiple Kinects, there must be enough distances among neighboring Kinects to avoid spoiled range data caused by the interference of their infrared speckle patterns. In the arrangement, their overlapped regions are too small to apply existing calibration methods using correspondences between their observations straightforwardly. Therefore, we propose a method to calibrate Kinects without large overlapped regions. In our method, first, we add extra RGB cameras in an environment to compensate overlapped regions. Thanks to them, we can estimate their camera parameters by obtaining correspondences between color images. Next, for accurate calibration, which considers range data as well as color images of Kinects, we optimize the estimated parameters by minimizing both the errors of correspondences between color images and those of range data of planar regions, which exist in a general environment such as walls and floors. Although our method consists of conventional techniques, its combination is optimized to achieve the calibration.

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Calibration of multi-Kinect and multi-camera setup for full 3D reconstruction

Cited by 10 publications

References 9 publications

A New Model of RGB-D Camera Calibration Based on 3D Control Field

A New Model of RGB-D Camera Calibration Based on 3D Control Field

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition

Calibration of multiple kinects with little overlap regions

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