SfM applications for 3D reconstruction from 2D avionics industrial inspection videos
A. Demi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.C. Van Gemert – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Y. Lin – Graduation committee member (TU Delft - Intelligent Vehicles)
Michael Weinmann – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
Structure-from-Motion (SfM) and Neural Radiance Fields (NeRFs) have significantly advanced 3D reconstruction in multi-view scenarios. Despite their success in handling non-repetitive, texture-rich scenes, applying such techniques to real-world scenarios with texture-less and repetitive structures remains challenging. One such case is in industrial inspection processes, specifically the reconstruction of aircraft engine blades. The goal is to build a 3D model of all the engine blades from a single video. We explore the application of SfM in modeling repetitive and texture-less blades, identify common causes of failure, and improve upon the default SfM by replacing the commonly used exhaustive match with sequential match to handle ambiguity stemming from repetitiveness. Sequential matching enables more precise pose estimation and better 3D reconstruction in our scenario. In addition, we explore the importance of choosing the correct camera model and provide a comparative look at the existing 3D mesh reconstruction solutions, presenting tweaked versions that result in better performance. This work lays the foundation for 3D reconstruction of repetitive and texture-less objects by proposing sequential matching, enabling better 3D modeling of engine blades compared to classic SfM pipelines.