Isolating the impact of shape from scattering/propagation

In Machine Learning (ML)-based object recognition, improvements in ML model performance are often attributed to both the quality of data and tuning of the model. However, determining which factor in object appearance is responsible for the improvement can be challenging. In contrast to a large number of works that are devoted to model improvements, we are focusing on properties in data which could help to isolate the impact of intrinsic object characteristics (such as shape) on the performance of ML models. The project employs three sources of data: collected data from aerial platforms, synthetic images generated by rendering CAD models, and lab-acquired images of 3D printed scale models from exactly the same CAD models. Our results demonstrate that even with limited real-world imagery, multi-source datasets significantly improve object recognition performance in untuned ML models. This approach, which enables tuning recognition to object shape rather than overall appearance, highlights the critical role of diverse data sources in enhancing ML object recognition capabilities.