Aida Farahani: From 2D to 3D in Seconds

With a specialization in 3D deep learning, Aida is doing ground-breaking work related to 3D simulations. In this episode, she first describes why meshes or point clouds are computationally-expensive for simulating changes to shapes. She then describes why “implicit fields” are much more efficient for this, as described in the widely-cited “DeepSDF paper.” Next she describes a special-case problem: Predicting the deformation of 2D objects into 3D shapes, such as when metal blanks are stamped into the shape of a car door, for example, which is a case that violates one of the assumptions for DeepSDF. However in this episode, Aida describes a method for using implicit fields to solve this problem too, thereby transforming a simulation process that used to take 20 minutes per trial to just a few seconds per run. In effect, this show is a preview of her soon-to-be-published doctoral dissertation!