Error analysis of 3D shape construction from structured lighting

Yang, Zaiming; Wang, Yuan-Fang

doi:10.1016/0031-3203(95)00076-3

Cited by 26 publications

(10 citation statements)

References 14 publications

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“…Most researches on error analysis in computer vision have been concerned with the analysis of the stereopsis technique and have considered only the quantization error [13], [31], [34], [36], [45]. We show in [50] how the sensor error model can be designed for the structured light sensing technique, which is the imaging technique used in our experiment. Assume that the error covariance matrix Λ is available [50], the sensor data integration process involves estimating the control vertices and updating the error covariance matrix (an accuracy measure) using the available shape constraints.…”

Section: The Modeling Schemementioning

confidence: 99%

“…We show in [50] how the sensor error model can be designed for the structured light sensing technique, which is the imaging technique used in our experiment. Assume that the error covariance matrix Λ is available [50], the sensor data integration process involves estimating the control vertices and updating the error covariance matrix (an accuracy measure) using the available shape constraints. Or we collect all 8 the shape constraints specified using Equations 3, 4, and 7, put them in a matrix form AP = B, and solve the equation by weighing each constraint with its expected accuracy [16], [33] …”

Section: The Modeling Schemementioning

confidence: 99%

“…Salient features of our modeling technique are: Using Kalman filter and the sensor error models developed in [50], we demonstrate a technique for sensor data fusion in a statistically optimal way; using the spline surface as the recording data structure, higher-order surface smoothness is achieved; our formulation still allows randomly-scattered heterogeneous sensor data to be used in model construction; and finally, the technique of adaptive refinment and state subdivision enables our modeling scheme to adapt to different degrees of complexity of the imaged structures.…”

Section: Introductionmentioning

confidence: 99%

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On 3D Model Construction by Fusing Heterogeneous Sensor Data

Wang¹

1994

Computer Vision and Image Understanding

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In this paper, we propose a scheme for 3D model construction by fusing heterogeneous sensor data. The proposed scheme is intended for use in an environment where multiple, heterogeneous sensors operate asynchronously. Surface depth, orientation, and curvature measurements obtained from multiple sensors and vantage points are incorporated to construct a computer description of the imaged object. The proposed scheme uses Kalman filter as the sensor data integration tool and hierarchical spline surface as the recording data structure. Kalman filter is used to obtain statistically optimal estimates of the imaged surface structure based on possibly noisy sensor measurements. Hierarchical spline surface is used as the representation scheme because it maintains high-order surface derivative continuity, may be adaptively refined, and is storage efficient. We show in this paper how these mathematical tools can be used in designing a modeling scheme to fuse heterogeneous sensor data.

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Section: The Modeling Schemementioning

confidence: 99%

Section: The Modeling Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On 3D Model Construction by Fusing Heterogeneous Sensor Data

Wang¹

1994

Computer Vision and Image Understanding

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“…5,6 For the SLS, calibration of system parameters is usually the first and crucial step because the calibration result determines final 3-D measurement precision directly. [7][8][9] To perform the triangulation, we need to know the intrinsic parameters of both projector and camera, as well as the extrinsic parameters between them. There have been several research works to address this classical problem.…”

Section: Introductionmentioning

confidence: 99%

Method for calibration accuracy improvement of projector-camera-based structured light system

Song

2017

Opt. Eng

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, "Method for calibration accuracy improvement of projector-camera-based structured light system," Opt. Eng. 56(7), 074101 (2017), doi: 10.1117/1.OE.56.7.074101. Abstract. Calibration is a critical step for the projector-camera-based structured light system (SLS).Conventional SLS calibration means usually use the calibrated camera to calibrate the projector device, and the optimization of calibration parameters is applied to minimize the two-dimensional (2-D) reprojection errors. A three-dimensional (3-D)-based method is proposed for the optimization of SLS calibration parameters. The system is first calibrated with traditional calibration methods to obtain the primary calibration parameters. Then, a reference plane with some precisely printed markers is used for the optimization of primary calibration results. Three metric error criteria are introduced to evaluate the 3-D reconstruction accuracy of the reference plane. By treating all the system parameters as a global optimization problem and using the primary calibration parameters as initial values, a nonlinear multiobjective optimization problem can be established and solved. Compared with conventional calibration methods that adopt the 2-D reprojection errors for the camera and projector separately, a global optimal calibration result can be obtained by the proposed calibration procedure. Experimental results showed that, with the optimized calibration parameters, measurement accuracy and 3-D reconstruction quality of the system can be greatly improved.

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“…Analyzing the deformation of the pattern on the image acquired by the stereo camera with respect to the projected one provides 3D information. 3,4 In our system, we use a laser emitter coupled with a diffraction diode, outputting pyramidalbeam rays. On a planar surface, the projected pattern is a regular grid of dots.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system

Dipandà

2004

J. Electron. Imaging

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This paper proposes a method for estimating the threedimensional (3D) rigid motion parameters from an image sequence of a moving object. The 3D surface measurement is achieved using an active stereovision system composed of a camera and a light projector, which illuminates the objects to be analyzed by a pyramidshaped laser beam. By associating the laser rays with the spots in the two-dimensional image, the 3D points corresponding to these spots are reconstructed. Each image of the sequence provides a set of 3D points, which is modeled by a B-spline surface. Therefore, estimating the 3D motion between two images of the sequence boils down to matching two B-spline surfaces. We consider the matching environment as an optimization problem and find an optimal solution using genetic algorithms. A chromosome is encoded by concatenating seven binary coded parameters, the angle, and the three components of the rotation vector axis, and the three translation vector components. We have defined an original fitness function for calculating the similarity measure between two surfaces. Experimental results with real and synthetic image sequences are presented to show the effectiveness and the robustness of the method. © 2004 SPIE and IS&T.

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Error analysis of 3D shape construction from structured lighting

Cited by 26 publications

References 14 publications

On 3D Model Construction by Fusing Heterogeneous Sensor Data

On 3D Model Construction by Fusing Heterogeneous Sensor Data

Method for calibration accuracy improvement of projector-camera-based structured light system

Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system

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