Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

Peng, Yeping; Qin, Shucong; Wang, Tao; Hu, Yixi; Nie, Shiping

doi:10.21203/rs.3.rs-2484432/v1

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…However, the parameterization requirements are high to ensure that the reconstructed surfaces can accurately reflect the shape and characteristics of the original data. Based on non-quadratic sampling discrete shear transform and Laplace algorithm to calculate the focus degree of image sequence pixels, and complete the 3D reconstruction of milling tool tip based on the out-of-focus shape principle [19]. The point clouds are aligned by RGB-D depth images in different time frames, thus enabling digital 3D reconstruction of the vehicle and endowing it with texture information [20].…”

Section: Introductionmentioning

confidence: 99%

Research on point cloud hole filling and 3D reconstruction in reflective area

Sun

WANG

Miao

et al. 2023

Preprint

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3D reconstruction is the process of obtaining the three-dimensional shape or surface structure of an object, which is widely used in advanced manufacturing fields such as automotive, aerospace, industrial inspection, and reverse engineering. However, due to the structural characteristics of the component itself, the reflective properties of the coating material, and other factors, there may be specular reflection during image acquisition, making it difficult to achieve complete 3D reconstruction of the component. This paper proposes a method to address the problem of incomplete 3D reconstruction of strongly reflective objects by recognizing outlier points and filling point cloud holes. The proposed View-Transform-PointNet outlier point recognition network improves the alignment of the initial point cloud plane and implements secondary alignment of the point cloud based on the perpendicularity between the outlier plane in mixed reflection and the point cloud plane. The point cloud hole-filling method is based on the principle of outlier formation and approximates a local Gaussian distribution to linear variation. The distance between the end of each outlier plane and the real surface is calculated to repair the depth information of outlier points. The proposed method achieves a 39.4% increase in the number of point cloud filling, a 45.2% increase in the number of triangular mesh faces, a 46.9% increase in surface area, and a chamfer distance (CD) of 0.4471009, which is better than existing geometric repair methods in terms of standard deviation and smoothness. The method improves the alignment of initial point cloud planes and enhances the accuracy of outlier point recognition, which are the main innovative points of this study. The 3D reconstruction of the repaired point cloud model is achieved through Poisson equation and parameter adjustment. The proposed method reduces the error caused by large curvature in the boundary region and improves the smoothness and accuracy of the reconstructed model.

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Section: Introductionmentioning

confidence: 99%