A non-contact measurement method of ship block using image-based 3D reconstruction technology

Wei, Yan; Ding, Zai-ran; Huang, Haocai; Yan, Chao; Leng, Jianxing

doi:10.1016/j.oceaneng.2019.03.015

Cited by 56 publications

(20 citation statements)

References 30 publications

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“…Besides, S j and S j denote the original and new point clouds, respectively. Their relationship is illustrated in equation (9).…”

Section: Preparation Of the Point Cloudsmentioning

confidence: 99%

3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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Deformation measurement of hull structure is of great significance for enhancing the quality of shipbuilding. In this paper, a three-dimensional (3D) reconstruction method based on RGB-D images is presented for non-structural surface of a stiffened hull plate. The algorithm for object surface region nodes (OSRN) is proposed to extract the features of point clouds and generate higher-order fitting surface model. Firstly, random sample consensus (RANSAC) algorithm and least square method are applied to eliminate the noise of point clouds. Spatial position of targets in RGB image and singular value decomposition (SVD) are utilized to accomplish the registration of point clouds from multiple camera views. Then, the point clouds data are clustered and fused to fit the quadric surface. On this basis, the 3D surface model is reconstructed through higher-order panel element. Two comparative experiments, including the measurements via the RGB-D camera and 3D Laser Scanner, are carried out to verify the applicability and accuracy of the proposed method.

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“…Besides, S j and S j denote the original and new point clouds, respectively. Their relationship is illustrated in equation (9).…”

Section: Preparation Of the Point Cloudsmentioning

confidence: 99%

3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…Different techniques have been proposed for reconstructing the 3D information for the scanned objects. These techniques can be categorized into active and passive techniques [3,4].…”

Section: Three Dimensional (3d)mentioning

confidence: 99%

3D Reconstruction Based on Fusing Active Structured Laser and Passive Stereo Techniques

Al-Saqal

Al-Temeemy

2020

NJES

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Three-dimensional reconstruction of real objects comprises capturing the appearance and the shape for these objects and determining the three-dimensional coordinates for their profiles. This reconstruction process can be accomplished either by using active or passive techniques. In this paper, a new fusion method is proposed for 3D reconstruction. This method exploits the advantages of both stereo-based passive and laser-based active techniques and overcomes their limitations to improve the performance of 3D reconstruction. With this method, a hybrid laser-based structured light scanning system is designed and implemented. This system captures the required information using passive and active techniques and uses the proposed fusion method for 3D reconstruction. The performance of the proposed method and its scanning system were experimentally evaluated. The evaluation results show high reconstruction performance for the proposed fusion method over the traditional 3D reconstruction techniques. The results also show the effectiveness of the hybrid laser scanning system and its ability to scan and reconstruct the shape and the appearance for real objects using the proposed fusion method.

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“…In addition, deep learning is computationally complex and memory consuming and requires a large amount of training time, which is not conducive to transferring deep learning models to practical application scenarios. erefore, it is of strong practical significance to improve the accuracy of point cloud classification segmentation, reduce memory loss, and improve the efficiency of training time [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Key Point Extraction Algorithm Based on Feature Space Value Filtering

Chen

2022

Mobile Information Systems

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With the rapid development of 3-dimensional (3D) acquisition technology, point clouds have a wide range of application prospects in the fields of computer vision, autonomous driving, and robotics. Point cloud data is widely used in many 3D scenes, and deep learning has become a mainstream research method for classification with the advantages of automatic feature extraction and strong generalization ability. In this paper, a hierarchical key point extraction framework is proposed to solve the problem of modeling the local geometric structure between points. Various point cloud models such as PointNet, PointNet++, and DGCNN are analyzed and their features in local key point are extracted. Based on these analyses, an indexed edge geometric feature spatial value screening neural network (IEGCNN) is proposed. This network extracts features from each point and its neighborhood, calculates the distance between the center point and the points within its neighborhood, and adds the point orientation information to the edge feature spatial value screening network. The relationship between points in the edge network architecture is projected onto a 3D coordinate system and decomposed into three orthogonal bases. The geometric structure between two points is modeled by feature aggregation based on the angle between the edge vector and the base vector and the distance between the center point and the neighboring points. The proposed method has the capability of fast processing of point cloud data by significantly reducing the training and recognition time. The experimental results show that this method achieved high classification accuracy value. This work also provides an idea to solve the problem of real-time target detection network, which has a broad applications prospect in the deployment of movable devices and real-time processing.

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A non-contact measurement method of ship block using image-based 3D reconstruction technology

Cited by 56 publications

References 30 publications

3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

3D Reconstruction Based on Fusing Active Structured Laser and Passive Stereo Techniques

Point Cloud Key Point Extraction Algorithm Based on Feature Space Value Filtering

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