Marker-Based 3D Reconstruction of Aggregates with a Comparative Analysis of 2D and 3D Morphologies

2026-03-13

AI Summary

This study addresses the challenge of efficiently and cost-effectively acquiring complete three-dimensional morphological information of aggregate particles in quarry or construction site environments, where traditional approaches fall short due to the limited accuracy of two-dimensional image analysis and the prohibitive cost of three-dimensional scanning equipment. To overcome these limitations, this work proposes a marker-assisted photogrammetric method that employs custom-designed markers to suppress background interference, enable multi-view point cloud registration, and provide scale calibration. The approach facilitates high-fidelity three-dimensional reconstruction of aggregates without requiring expensive instrumentation. Experimental validation confirms the method’s accuracy and systematically demonstrates significant discrepancies between two-dimensional and three-dimensional morphological parameters, offering a low-cost, highly flexible solution for aggregate quality assessment and morphological research.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Web measurements
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Aggregates, serving as the main skeleton in assemblies of construction materials, are important functional components in various building and transportation infrastructures. They can be used in unbound layer applications, e.g. pavement base and railroad ballast, bound applications of cement concrete and asphalt concrete, and as riprap and large-sized primary crushed rocks. Information on the size and shape or morphology of aggregates can greatly facilitate the Quality Assurance/Quality Control (QA/QC) process by providing insights of aggregate behavior during composition and packing. A full 3D characterization of aggregate particle morphology is difficult both during production in a quarry and at a construction site. Many aggregate imaging approaches have been developed to quantify the particle morphology by computer vision, including 2D image-based approaches that analyze particle silhouettes and 3D scanning-based methods that require expensive devices such as 3D laser scanners or X-Ray Computed Tomography (CT) equipment. This paper presents a flexible and cost-effective photogrammetry-based approach for the 3D reconstruction of aggregate particles. The proposed approach follows a marker-based design that enables background suppression, point cloud stitching, and scale referencing to obtain high-quality aggregate models. The accuracy of the reconstruction results was validated against ground-truth for selected aggregate samples. Comparative analyses were conducted on 2D and 3D morphological properties of the selected samples. Significant differences were found between the 2D and 3D statistics. Based on the presented approach, 3D shape information of aggregates can be obtained easily and at a low cost, thus allowing convenient aggregate inspection, data collection, and 3D morphological analysis.

Problem

• 3D reconstruction
• aggregate morphology
• photogrammetry
• 2D vs 3D analysis
• cost-effective characterization

Innovation

• marker-based photogrammetry
• 3D reconstruction
• aggregate morphology
• cost-effective imaging
• point cloud stitching