Computer Science

CLC number: TP23

3D Surface Reconstruction of Coarse Aggregate Particles from Occlusion-Free Multi-View Images

无遮挡多视角成像粗集料颗粒三维表面重建方法

¹ School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
² Office of Infrastructure, Chang'an University, Xi'an 710064, Shaanxi, China
³ School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

^† Corresponding author. E-mail: zhaoyunsun@126.com (SUN Zhaoyun), gjxing@chd.edu.cn (GUO Jianxing)

Received: 28 January 2024

Abstract

Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering. This article introduces an innovative system for the three-dimensional (3D) surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging. The system captures synchronized images of particles in free fall, employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices. A pre-trained segmentation model is utilized to eliminate the background of the images. The Shape from Silhouettes (SfS) algorithm is then applied to generate 3D voxel data, followed by the Marching Cubes algorithm to construct the 3D surface contour. Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy, with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.

摘要

快速准确地评估粗集料颗粒的几何特性对保证公路工程中路面性能至关重要。本文构建了一种基于无遮挡多视角成像技术的粗集料颗粒三维表面轮廓重建系统,能够同步捕捉自由下落状态下粗集料的多视角图像。采用黑色哑光球体和非线性优化方法实现对多视角成像系统中相机投影矩阵的标定。使用预训练的分割模型去除多视角图像中的背景干扰,应用剪影建模算法生成粗集料颗粒的三维体素数据, 使用行进立方体算法构建其三维表面轮廓。对标准部件和不同的粗集料颗粒进行了测量和对比分析,结果表明, 本系统对粗集料颗粒的平均测量精度达到0.434毫米, 实现了亚毫米精度测量, 并且显著提高了扫描和重建效率。