Zachary Teed - Optimization Inspired Neural Networks for Multiview 3D

Oct 5th, 2021 @ MIT CSAIL Abstract: Multiview 3D has traditionally been approached as an optimization problem. The solution is produced by an algorithm which searches over continuous variables (camera pose, depth, 3D points) to satisfy both geometric constraints and visual observations. In contrast, deep learning offers an alternative strategy where the solution is produced by a general-purpose network with learned weights. In this talk, I will be discussing a hybrid approach for multiview problems, where we explore neural architecture designs inspired by optimization. We’ve used this general strategy to develop accurate and robust systems for optical flow, stereo, scene flow, and visual SLAM. Bio: Zachary Teed is a 4th year PhD student at Princeton University. He is a member of the Princeton Vision and Learning Lab and advised by Professor Jia Deng. His research focuses on problems in multiview perception including optical flow, stereo, scene flow, and visual SLAM. Previously, Zachary graduated from Washington University in St. Louis with a B.S. in computer science. He has received several awards including the Qualcomm Innovation Fellowship, the Jacobus Fellowship, and the ECCV 2020 Best Paper Award.