We are a computer graphics collective at UChicago led by Rana Hanocka, pursuing innovation at the intersection of 3D and Deep Learning.
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Our research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep Learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and we are interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.
We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) - and are interested in broader applications related to these areas. Additional research directions that we are aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more interpretable and explainable.
Follow our github organization for updates on our latest research.