Surface reconstruction from unoriented point clouds by a new triangle selection strategy

Wang, Wen; Su, Tianyun; Liu, Haixing; Li, Xinfang; Jia, Zhen; Zhou, Lin; Song, Zhuanling; Ding, Ming

doi:10.1016/j.cag.2019.08.002

Cited by 28 publications

(3 citation statements)

References 19 publications

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“…Hou and Gu [5] studied the triangular mesh generation of scattered points in space and realized uniform mesh reconstruction using a micro incremental method. Wang et al [6] used the angle condition of adjacent triangles and the disjoint condition of triangles to generate surfaces from a triangular mesh. The mesh model has strong flexibility and good adaptability to the surface boundary, but it also has many shortcomings, including a large number of mesh patches, large memory resources, and low reconstruction accuracy.…”

Section: Model Construction and Enhancementmentioning

confidence: 99%

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Zhu

Dong

Shen

et al. 2023

Tsinghua Sci. Technol.

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The accuracy and efficiency of three-dimensional (3D) surface forming, which directly affects the cycle and quality of production, is important in manufacturing. In practice, given the uncertainty of metal plate springback, an error exists between the actual plate and the target surface, which creates a nonlinear mapping from computer aided design models to bending surfaces. Technicians need to reconfigure parameters and process a surface multiple times to delicately control springback, which greatly wastes human and material resources. This study aims to address the springback control problem to improve the efficiency and accuracy of sheet metal forming.A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface bending. To address the sample data shortage problem, we put forward an advanced approach by combining a deep learning model with case-based reasoning (CBR). Next, a multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach through surface data preprocessing, CBR-based model matching, convolution neural network-based machining surface generation, and bending parameter generation with a series of model transformations. Moreover, the proposed approaches and the framework are verified by considering saddle surface processing as an example. Overall, this study provides a new idea to improve the accuracy and efficiency of surface processing.

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Section: Model Construction and Enhancementmentioning

confidence: 99%

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Zhu

Dong

Shen

et al. 2023

Tsinghua Sci. Technol.

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“…Peethambaran [35] built a graph structure to reconstruct a watertight surface. Wang [36] proposed a new triangle selection strategy to reduce the topology errors and holes in Delaunay triangulation. Liu [37] extended the Delaunay-based strategy to image stitching.…”

Section: B Delaunay-based Approachesmentioning

confidence: 99%

Voxel Structure-Based Mesh Reconstruction From a 3D Point Cloud

Wang

Zhao

2022

IEEE Trans. Multimedia

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Mesh reconstruction from a 3D point cloud is an important topic in the fields of computer graphic, computer vision, and multimedia analysis. In this paper, we propose a voxel structure-based mesh reconstruction framework. It provides the intrinsic metric to improve the accuracy of local region detection. Based on the detected local regions, an initial reconstructed mesh can be obtained. With the mesh optimization in our framework, the initial reconstructed mesh is optimized into an isotropic one with the important geometric features such as external and internal edges. The experimental results indicate that our framework shows great advantages over peer ones in terms of mesh quality, geometric feature keeping, and processing speed. The source code of the proposed method is publicly available at: github.com/vvvwo/Parallel-Structure-for-Meshing.

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“…However, several studies [49,50] implement these tools for specific objects, but not for building construction units. Finally, the work by Tommasi et al [51] brings together diverse software in the market for reverse engineering in BIM applied to historical elements, whereas Pocobelli et al [52] integrated organised moisture data into spreadsheets and associated it with parametric objects development through Dynamo. In addition, there are different reconstruction methods in the scientific literature [53][54][55][56][57][58][59][60][61], most of which use the Delaunay triangulation [54]. Surface reconstruction is the process of rebuilding geometry from the scanned point cloud.…”

Section: Related Workmentioning

confidence: 99%

From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

et al. 2020

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Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

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Surface reconstruction from unoriented point clouds by a new triangle selection strategy

Cited by 28 publications

References 19 publications

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

Voxel Structure-Based Mesh Reconstruction From a 3D Point Cloud

From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

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