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.

Mind wandering occurs when a person’s attention unintentionally shifts away from their current thought or task. Being able to automatically detect cases of mind wandering can assist applications with attention retention, and help people with maintaining focus. Many methods have been tested to deal with mind-wandering detection, but they are mainly conducted in controlled environments.
There also has been little study into the usefulness of learned features from neural networks. This paper is focused on showcasing the effectiveness of
using neural network-generated features as input for classification models. Specifically, using ResNet to generate features which are then used as input by supervised learning models for classification. These features and models were used to classify mind wandering in the Mementos data set, outside of a controlled environment or differently put as “In-the-wild“. The study shows that the extracted features could not be used to accurately detect mind wandering based on the F1-Score (Macro) measure. The results can be attributed to data imbalance, low amount of data, lack of dataset-tailored pre-processing operations, and indiscriminate features. To improve on the study, more data collection is advised and the usage of methods like re-sampling and data augmentation to deal with data imbalance. And lastly, experimentation with neural network training and transforming the data into a time series format to better represent the temporal information from the data.