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.

Convolutional Neural Networks are particularly vulnerable to attacks that manipulate their data, which are usually called adversarial attacks. In this paper, a method of filtering images using the Fast Fourier Transform is explored, along with its potential to be used as a defense mechanism to such attacks. The main contribution that differs from other methods that use the Fourier Transform as a filtering element in neural networks is the use of labeled data to determine how to filter the pictures. This paper concludes that, while the filtering proposed is hardly better than a simple low-pass filter, it still manages to improve resistance to adversarial attacks with a minimal drop in the standard accuracy of the network.