As our world comprises a diverse range of 3D objects, understanding them in their complete, three-dimensional form is now made possible with advancements in computing power, in contrast to the limiting representation of 2D projections. The central aim of 3D shape analysis is to investigate the geometric features of 3D objects, which is an interdisciplinary field that includes sub-fields of computer science such as computer vision, machine learning, computer graphics, and computational geometry. Our team is dedicated to creating innovative algorithms for imaging data on non-regular grids (see [Research Areas] for more information). 

Applications of 3D shape analysis

3D shape analysis has broad applications across multiple disciplines, including medical imaging, biology, ergonomics, robotics, material/chemical engineering, etc., providing valuable shape markers that can offer a deeper understanding of complex structural patterns from a geometric perspective. Our team is interested in a variety of research domains, which can be found at  [Research Domains].

Why many brain applications?

The majority of our research focuses on investigating brain shapes, which are among the most complex shapes found in nature. Due to the brain's intricate geometric patterns, standard computer vision and machine learning techniques may not effectively model it. While our work supports neuroscience research on human cognition, behavior, and brain diseases/disorders, our main interest lies in developing new shape processing algorithms. We are open to exploring 3D shape analysis in various fields and welcome prospective team members interested in algorithm development for 3D object processing using computer vision and machine learning. 

Open-source support