Image-based 3D reconstruction has been employed in industrial metrology for micro measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces (textureless, shiny, and transparent) is still an open issue. This thesis presents various methodologies to successfully generate a highly-detailed and reliable 3D reconstruction of non-collaborative objects using the proposed photometric stereo image acquisition system. The first proposed method employs geometric construction to integrate photogrammetry and photometric stereo in order to overcome each technique's limitations and to leverage each technique's strengths in order to reconstruct an accurate and high-resolution topography of non-collaborative surfaces. This method uses accurate photogrammetric 3D measurements to rectify the global shape deviation of photometric stereo meanwhile uses photometric stereo to recover the high detailed topography of the object. The second method combines the high spatial frequencies of photometric stereo depth map with the low frequencies of photogrammetric depth map in frequency domain to produce accurate low frequencies while retaining high frequencies. For the third approach, we utilize light directionality to improve texture quality by leveraging shade and shadow phenomena using the proposed image-capturing system that employs several light sources for highlighting roughness and microstructures on the surface. And finally, we present two methods that effectively orient images by leveraging the low-contrast textures highlighted on object surfaces (roughness and 3D microstructures) using proper lighting system. Various objects with different surface characteristics including textureless, reflective, and transparent are used to evaluate different proposed approaches. To assess the accuracy of each approach, a comprehensive comparison between reference data and generated 3D points is provided.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/373307 |
| Date | 11 April 2023 |
| Creators | Karami, Ali |
| Contributors | Menna, Fabio, Karami, Ali |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:187, numberofpages:187, alleditors:Menna, Fabio |
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