This project is carried out as part of the EDSGN-561 course at Penn State during the Spring 2021 semester. The outcomes presented within the scope of this research can be accessed here. The project aims to investigate learning-based models trained on problem-specific datasets to facilitate creative inquiries by providing design suggestions. By doing so, we aim to address the gap between task-oriented generic solutions provided by current CAD systems and the reciprocal and problem-specific nature of the act of designing.

The Autoencoder architecture used in this research is based on a previous research titled "Learning Representations and Generative Models for 3D Point Clouds" by Achlioptas et al. (arXiv | GitHub)