Calibration Between Depth and Color Sensors for Commodity Depth Cameras

Cha, Zhang; Zhang, Zhengyou

doi:10.1007/978-3-319-08651-4_3

Cited by 65 publications

(58 citation statements)

References 20 publications

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“…Herrera et al [16] and [17] presented a method for aligning the RGB image with depth map using the point-on-plane constraint without any depth corrections. As an extension, [14] added a depth distortion model that is dependent on the image space rather than object space in their total system calibration and showed superior performance compared to [13].…”

Section: Introductionmentioning

confidence: 99%

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Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

Chow

Lichti

2013

IEEE Access

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The Kinect system is arguably the most popular 3-D camera technology currently on the market. Its application domain is vast and has been deployed in scenarios where accurate geometric measurements are needed. Regarding the PrimeSense technology, a limited amount of work has been devoted to calibrating the Kinect, especially the depth data. The Kinect is, however, inevitably prone to distortions, as independently confirmed by numerous users. An effective method for improving the quality of the Kinect system is by modeling the sensor's systematic errors using bundle adjustment. In this paper, a method for modeling the intrinsic and extrinsic parameters of the infrared and colour cameras, and more importantly the distortions in the depth image, is presented. Through an integrated marker-and feature-based self-calibration, two Kinects were calibrated. A novel approach for modeling the depth systematic errors as a function of lens distortion and relative orientation parameters is shown to be effective. The results show improvements in geometric accuracy up to 53% compared with uncalibrated point clouds captured using the popular software RGBDemo. Systematic depth discontinuities were also reduced and in the check-plane analysis the noise of the Kinect point cloud was reduced by 17%.INDEX TERMS Kinect, camera calibration, quality assurance, quantization, 3D/stereo scene analysis.

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Section: Introductionmentioning

confidence: 99%

“…Staranowicz and Mariottini [19] compared the approach of [14] and portions of the [17] calibration to a calibration method that uses spheres instead of planes. Their results agreed with [18] and indicated weak recovery of relative orientation parameters between the depth and RGB image when using the point-on-plane approach.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

Chow

Lichti

2013

IEEE Access

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“…The infrared projector pattern on a predefined plane (Z 0 ) used in in-factory calibration [14] was stored in the sensor firmware. While capturing the real feature ( Q i ), both the IR standard projector pattern (

x_{i, 0}^{c}

) and the real IR projector captured by IR camera (

x_{i}^{c}

) are identified.…”

Section: A Distortion Calibration Model For Depth Sensormentioning

confidence: 99%

“…Zhang’s [14] method is such a type of calibration; the author used the maximum likelihood estimation to calibrate color internal parameters and the manufacture parameters for a depth sensor without any distortion effect. The main limitation of this method is the distortion parameters for both IR cameras and projectors, which are not estimated or compensated.…”

Section: Introductionmentioning

confidence: 99%

A New Calibration Method for Commercial RGB-D Sensors

Darwish

Tang

et al. 2017

Sensors

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Commercial RGB-D sensors such as Kinect and Structure Sensors have been widely used in the game industry, where geometric fidelity is not of utmost importance. For applications in which high quality 3D is required, i.e., 3D building models of centimeter-level accuracy, accurate and reliable calibrations of these sensors are required. This paper presents a new model for calibrating the depth measurements of RGB-D sensors based on the structured light concept. Additionally, a new automatic method is proposed for the calibration of all RGB-D parameters, including internal calibration parameters for all cameras, the baseline between the infrared and RGB cameras, and the depth error model. When compared with traditional calibration methods, this new model shows a significant improvement in depth precision for both near and far ranges.

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“…In [15], a method to register a pair of images captured from visible and IR sensors by line and point matching is presented. In addition, calibration between depth and color sensors by a maximum likelihood solution by using checker board is performed in [16,17]. In [18], a multiple sensor fusion system, which combines RGB-Dvision, lasers and a thermal sensor, in order to detect people, in a mobile robot.…”

Section: Related Workmentioning

confidence: 99%

3D Multi-Spectrum Sensor System with Face Recognition

Kim

et al. 2013

Sensors

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This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained.

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Calibration Between Depth and Color Sensors for Commodity Depth Cameras

Cited by 65 publications

References 20 publications

Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

A New Calibration Method for Commercial RGB-D Sensors

3D Multi-Spectrum Sensor System with Face Recognition

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