[2603.03654] Field imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03654 (cs) [Submitted on 4 Mar 2026] Title:Field imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications Authors:Haohang Huang View a PDF of the paper titled Field imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications, by Haohang Huang View PDF Abstract:Construction aggregates, including sand and gravel, crushed stone and riprap, are the core building blocks of the construction industry. State-of-the-practice characterization methods mainly relies on visual inspection and manual measurement. State-of-the-art aggregate imaging methods have limitations that are only applicable to regular-sized aggregates under well-controlled conditions. This dissertation addresses these major challenges by developing a field imaging framework for the morphological characterization of aggregates as a multi-scenario solution. For individual and non-overlapping aggregates, a field imaging system was designed and the associated segmentation and volume estimation algorithms were developed. For 2D image analyses of aggregates in stockpiles, an automated 2D instance segmentation and morphological analysis approach was established. For 3D point cloud analyses of aggregate stockpiles, an integrated 3D Reconstruction-Segmentation-Completion (RSC-3D) approach was established: 3D reconstruction procedures from m...