Lahav Lipson will present his FPO " Fast and Robust 3D Reconstruction " on Thursday, November 21, 2024 at 10:00 AM in Friend 202 . Location: Friend 202 The members of Lahav ’s committee are as follows: Examiners: Jia Deng (Adviser), Adam Finkelstein , Szymon Rusinkiewicz Readers: Anirudha Majumdar , Olga Russakovsky A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: 3Dreconstruction from visual data is an important subtask for robotics, autonomous machines, and 3D scene understanding. It involves estimating camera and object motion from images/video, as well as 3D structure. I will introduce an approach known as Optimization Guided Neural Iterations (OGNI), and demonstrate how it can be applied to various 3D reconstruction tasks. In OGNI-based approaches, we mimic classical optimization algorithms by breaking down each task into a series of small revisions predicted by a shallow network. Each revision is supervised independently, and is informed by features conditioned on a running estimate of the solution. This mechanism is surprisingly general, and leads to robust and efficient solutions to 3D reconstruction. Moreover, I introduce several explicit optimization layers which enable one to reformat these challenging problems into easier, low-level vision tasks. On visual SLAM, stereo matching and object pose estimation, I show how this approach leads to state-of-the art accuracy and/or speed. I also discuss potential future directions for this line of work.