AI-aided design for industrial manufacturing: generating synthetic image datasets to train 3D object reconstruction neural networks

Industrial manufacturing faces many challenges and opportunities as novel technologies change how products are designed and produced. The design step of a product requires skills and time, starting from conceptualizing the object's 3D shape. However, AI models have been proven capable of reconstructing 3D models from images. Thus, a designer may approach the modeling phase of a product with traditional CAD software, relying not only on existing 3D models but also on the digitalization of everyday real objects, prototypes, or photographs. However, AI models need to be trained on extensive datasets to obtain reliable behaviors, and the manual creation of such datasets is usually time-consuming. Synthetic datasets could speed up the model's training process, providing automatically labeled data for the objects of interest for the designer. This research explores a novel approach to foster synthetic dataset generation for 3D object reconstruction. The proposed pipeline involves setting up 3D models and customizing the rendering pipeline to create datasets with different rendering properties automatically. These datasets are then used to train and test a 3D object reconstruction model to investigate how to improve synthetic dataset generation to optimize performance.