Sun Chao, Miao LongXin, Wang MeiYuan, Shi Jiuye, Ding JianJun
State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China.
College of Intelligent Manufacturing, Jianghan University, Wuhan, 430056, China.
Sci Rep. 2023 Oct 28;13(1):18524. doi: 10.1038/s41598-023-45648-5.
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.
三维重建是获取物体三维形状或表面结构的过程，广泛应用于汽车、航空航天、工业检测和逆向工程等先进制造领域。然而，由于部件本身的结构特性、涂层材料的反射特性等因素，在图像采集过程中可能会出现镜面反射，导致难以实现部件的完整三维重建。本文提出了一种通过识别离群点和填充点云孔洞来解决强反射物体三维重建不完整问题的方法。所提出的视图变换点云网络（View-Transform-PointNet）离群点识别网络改善了初始点云平面的对齐，并基于混合反射中的离群平面与点云平面之间的垂直性实现了点云的二次对齐。点云孔洞填充方法基于离群点形成的原理，将局部高斯分布近似为线性变化。计算每个离群平面末端与真实表面之间的距离，以修复离群点的深度信息。所提出的方法在点云填充数量上增加了39.4%，三角网格面数量增加了45.2%，表面积增加了46.9%，倒角距离（CD）为0.4471009，在标准差和平滑度方面优于现有的几何修复方法。该方法改善了初始点云平面的对齐，提高了离群点识别的准确性，这是本研究的主要创新点。通过泊松方程和参数调整实现了修复后点云模型的三维重建。所提出的方法减少了边界区域大曲率引起的误差，提高了重建模型的平滑度和准确性。
