Splicing of Multi-View Point Clouds Based on Calibrated Parameters of Turntable

Wei, 郎威 Lang; Junpeng, 薛俊鹏 Xue; Chenghang, 李承杭 Li; Qican, 张启灿 Zhang

doi:10.3788/cjl201946.1104003

Cited by 7 publications

(4 citation statements)

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“…In a multi-camera system, each camera possesses specific internal parameters (such as focal length and principal point) and external parameters (such as position and orientation). The objective of global parameter calibration is to determine both the internal and external parameters for each camera and to establish the relationships between them [9] [10] [11].…”

Section: Multi Camera Global Parameter Calibrationmentioning

confidence: 99%

Global Calibration Method for Monocular Multi-View System Using Rotational Dual-Target

He,

2024

OJAppS

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To make the problems of existing high requirements of calibration tools, complex global calibration process addressed for monocular multi-view visual measurement system during measurement, in the paper, a global calibration method is proposed for the geometric properties of rotational correlation motion and the absolute orientation of the field of view without over lap. Firstly, a dual-camera system is constructed for photographing and collecting the rotating image sequence of two flat targets rigidly connected by a long rod at different positions, and based on the known parameters, such as, target feature image, world coordinates, camera internal parameters and so on, then the global PnP optimization method is used to solve the rotation axis and the reference point at different positions; Then, the absolute orientation matrix is constructed based on the parameters of rotation axis, reference point and connecting rod length obtained by this method. In the end, the singular value decomposition method is used to find the optimal rotation matrix, and then get the translation matrix. It's shown based on simulation and actual tests that in comparison with the existing methods, the maximum attitude and pose errors is 0.0083˚ and 0.3657 mm, respectively, which improves the accuracy by 27.8% and 24.4%, respectively. The calibration device in this paper is simple, and there are no parallel, vertical and coplanar requirements between multiple rotating positions. At the same time, in view of the calibration accuracy, the accuracy requirements of most application scenarios can be met.

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Section: Multi Camera Global Parameter Calibrationmentioning

confidence: 99%

Global Calibration Method for Monocular Multi-View System Using Rotational Dual-Target

He,

2024

OJAppS

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“…In the existing 3D reconstruction system of structured light, the position of the measured object and the imaging device is relatively fixed so the measured object's surface shape information can only be obtained from a single perspective. If a complete threedimensional model of the object surface needs to be built, the measured object must be photographed and measured from multiple angles [29,30]. Since the measurement coordinates systems of local 3D point cloud data in a single shot differ, coordinate changes are needed to achieve a rough registration of point cloud data to unify the obtained 3D point cloud data into the same coordinate system.…”

Section: Calculation Of Rotation Matrixmentioning

confidence: 99%

Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion

Feng,

Wu,

Liu

et al. 2023

Sensors

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Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.

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“…By synergizing a rotary platform with a tri-coordinate measuring apparatus, they harnessed acquired spatial axial data for point cloud registration, the discriminant error of the focal evaluation function will have some influence on the experimental results. Lang et al [20] presented an approach leveraging turntable parameter calibration outcomes to assist the preliminary stitching of multi-view 3D point clouds. However, this method can only obtain the position relationship between 3D point clouds in a specific perspective.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, see figure 1, this study presents a novel multi-view point cloud registration approach anchored in a unified multi-view spatial coordinate system-ICP (MSCS-ICP). This method is not limited by the position relationship of the 3D point cloud [20], and the relationship between the 2D camera sensors is mapped to the 3D coordinate system for unification [21]. In this paper, the rotating spatial rotation axis is used to calibrate the position relationship between the world coordinate system of the camera, and a good initial position is given to the 3D point cloud under different angles.…”

Section: Introductionmentioning

confidence: 99%

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

Song,

Zhang,

Luo

et al. 2024

Meas. Sci. Technol.

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The effectiveness of point cloud registration critically determines 3D reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on Multi-view Spatial Coordinate System ICP to solve the problem of- 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3 to1.9m, our proposed method boasts a maximum error of just 0.069mm, an average absolute error of 0.039mm, and a root mean square error of 0.043mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.

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Splicing of Multi-View Point Clouds Based on Calibrated Parameters of Turntable

Cited by 7 publications

References 0 publications

Global Calibration Method for Monocular Multi-View System Using Rotational Dual-Target

Global Calibration Method for Monocular Multi-View System Using Rotational Dual-Target

Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

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