Ilya Chugunov will present his General Exam "Implicit Inverse Imaging with Explicit Priors" on Monday, April 18, 2022 at 10 am in Friend Center 202 and via Zoom. Zoom link: [ https://princeton.zoom.us/j/91472663309 | https://princeton.zoom.us/j/91472663309 ] Committee Members: Felix Heide (advisor), Szymon Rusinkiewicz, Jia Deng Abstract: Armed with the right math, we can solve many tough imaging problems analytically, with no train time and no train data. Unfortunately, from lens aberrations to high level semantic features, the real visual world is full of things we either can’t or don’t want to model, but which we can learn from data. After all, we’ve yet to mathematically define a dog, but even a simple U-Net can be trained to recognize one. In this exam I present three works which try to combine analytic priors with a learned reconstruction pipeline: Mask-ToF, which uses differentiable simulation of time-of-flight imaging to help learn a physical augmentation to depth camera hardware in order to reduce imaging artifacts; GHz-ToF, which uses this same simulation to train a network for correction ambiguity in phase measurements; and my most recent work on handheld neural depth refinement, wherein we train an implicit model to recover fine depth of tabletop objects from a single smartphone camera snapshot. Reading List: [ https://docs.google.com/document/d/1tLs9TnnLzUGX6ebPPVpXsvLgsiCirsKl2Jxo_ji-... | https://docs.google.com/document/d/1tLs9TnnLzUGX6ebPPVpXsvLgsiCirsKl2Jxo_ji-... ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.