Synthetic Shape

Synthetic shape generation focuses on creating realistic 3D models from data, primarily addressing challenges in representing complex shapes and establishing correspondences between them. Current research heavily utilizes deep learning, particularly employing graph neural networks and transformers to improve accuracy and efficiency in tasks like surface normal estimation and shape matching. These advancements are crucial for applications such as in-silico clinical trials in medicine, enabling cost-effective validation of medical devices and procedures through the generation of realistic anatomical models. The field is also exploring unsupervised learning techniques to leverage large, unlabeled datasets for improved shape reconstruction from single views.