Wed, Oct 05, 2022 @ 10:00 AM - 11:00 AM
PhD Candidate: Shichen Liu
Title: Fast and Accurate Geometry Inference with Learned Recurrent Optimizers
Time: Oct. 5th, 2022, 10am - 11am
Committee: Yajie Zhao, Randall Hill, Stefanos Nikolaidis, Andrew Nealen, Aiichiro Nakano
Geometry inference requires accuracy, efficiency, and robustness in various use cases, such as AR/VR, autonomous driving, etc. Compared to traditional methods, deep convolutional neural networks have achieved noticeable improvement on a wide range of geometry inference tasks in terms of robustness. However, these models either produce inaccurate predictions or have a large computation overhead due to specially designed structures, which are hard to be deployed into a real-world system. In this thesis proposal, I introduce a framework that can largely improve the inference speed with high accuracy for geometry inference by taking the vanishing point detection task as a case study.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon