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Exploiting Light Directionality for Image‐Based 3D Reconstruction of Non‐Collaborative Surfaces
Author(s) -
Karami Ali,
Menna Fabio,
Remondino Fabio,
Varshosaz Masood
Publication year - 2022
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12400
Subject(s) - structured light , photogrammetry , point cloud , computer vision , 3d reconstruction , artificial intelligence , computer science , point (geometry) , matching (statistics) , surface roughness , root mean square , optics , materials science , mathematics , physics , geometry , statistics , quantum mechanics , composite material
Abstract Three‐dimensional (3D) measurement of non‐collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image‐matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre‐processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image‐matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting.