Methodology of Object Reconstruction by Photogrammetry and Structured-Light Scanning for Industrial 3D Visualisation

Abstract

In the context of accelerating digitalization, reliable object reconstruction represents a key prerequisite for developing accurate and functional digital twins. This study introduces a unified evaluation methodology designed to assess and compare optical 3D scanning technologies in terms of geometric accuracy, data completeness, and model consistency. The framework integrates all essential stages of digital reconstruction—from data acquisition to quantitative validation—ensuring reproducibility and comparability of results across different optical systems. To verify its applicability, two optical principles, photogrammetry and structured-light scanning, were implemented on the autonomous mobile robot MiR100. The reference CAD model in a 1:1 scale served as the ground-truth geometry for all analyses. Evaluation procedures included visual inspection, dimensional measurements, and statistical error analysis performed in MeshLab, CloudCompare, and MATLAB. The results confirmed that photogrammetry provides high-quality textural detail but suffers from geometric noise and scale drift (relative error > 10%), whereas structured-light scanning delivers more stable and metrically accurate results. In particular, the scanner mode achieved the highest precision, with a mean deviation of 17.4 mm, RMSE of 26.8 mm, and relative error of 7.6%. The proposed methodological framework thus establishes a reproducible basis for evaluating 3D reconstruction accuracy and supports the integration of optimized digital models into digital twin environments.