Three-dimensional reconstruction and segmentation of an aggregate stockpile for size and shape analyses

2026-03-04

AI Summary

This study addresses the challenge of performing convenient, low-cost three-dimensional (3D) size and shape analysis of large aggregates within stockpiles—a task that existing field methods struggle with, often being limited to individual or manually separated particles. To overcome this limitation, the authors propose a non-contact, in-situ approach based on smartphone imagery. The method first reconstructs a 3D point cloud of the entire aggregate stockpile using Structure-from-Motion (SfM), followed by automated extraction of individual aggregates through 3D segmentation algorithms. This work represents the first integration of SfM and 3D segmentation for simultaneous stockpile reconstruction and particle isolation. Preliminary experiments demonstrate the feasibility of the approach in capturing detailed 3D geometric information of aggregates, offering a promising, cost-effective, and efficient solution for on-site quality assurance and quality control (QA/QC) in construction and materials applications.

Technology Category

• Computer Vision: Segmentation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Web measurements

Abstract

Aggregate size and shape are key properties for determining quality of aggregate materials used in road construction and transportation geotechnics applications. The composition and packing, layer stiffness, and load response are all influenced by these morphological characteristics of aggregates. Many aggregate imaging systems developed to date only focus on analyses of individual or manually separated aggregate particles. There is a need to develop a convenient and affordable system for acquiring 3D aggregate information from stockpiles in the field. This paper presents an innovative 3D imaging approach for potential field evaluation of large-sized aggregates, whereby engineers can perform inspection by taking videos/images with mobile devices such as smartphone cameras. The approach leverages Structure-from-Motion (SfM) techniques to reconstruct the stockpile surface as 3D spatial data, i.e. point cloud, and uses a 3D segmentation algorithm to separate and extract individual aggregates from the reconstructed stockpile. The preliminary results presented in this paper demonstrate the future potential of using 3D aggregate size and shape information for onsite Quality Assurance/Quality Control (QA/QC) tasks.

Problem

• 3D reconstruction
• aggregate stockpile
• size and shape analysis
• field imaging
• quality assurance

Innovation

• 3D reconstruction
• aggregate segmentation
• Structure-from-Motion
• mobile imaging
• QA/QC