Optimizing close-range photogrammetry solutions for network configuration with accessibility-related constraints

Year 2026, Volume: 11 Issue: 1, 54 - 63, 01.10.2025

Nur Nazura Abd Razak , Mohd Azwan Abbas , Muhamad Aliff Haikal Kamaruzzaman , Albertkon-fook Chong , Norshahrizan Mohd Hashim

https://doi.org/10.26833/ijeg.1645760

Abstract

Renowned for its ability to attain high-accuracy data, close-range photogrammetry (CRP) is a reliable technique for 3D measurements of both exterior and interior surfaces. However, the accessibility-related constraints require different imaging configurations (i.e., network design) when using a digital camera to effectively reconstruct complete interior surfaces with the CRP approach. Given these limitations, this study aimed to determine the optimal digital image matching configuration that maintains the quality of CRP measurements. To address this, experiments were conducted in a test field that designed to reflect these constraints. The methodology consisted of four main components: i) the selection of the study area; ii) image acquisition; iii) image processing (i.e., image-matching); and iv) evaluation. Digital image matching was performed using two different approaches: i) the consecutive network and ii) the L-shape network (separated image matching). To validate the methods, their accuracy was assessed using two procedures: the establishment of scale bars and independent vectors from targets. Based on the calculated vectors, the L-shape network demonstrated up to 47% higher accuracy compared to the consecutive network. Consequently, it can be concluded that the L-shape network is an effective processing method for overcoming spatial constraints in CRP measurements.

Keywords

Close-Range Photogrammetry , Digital Image Matching , Geometric Constraints , Interior Surfaces Reconstruction , Accuracy

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