Events Calendar

PhD Thesis Proposal - Shichen Liu

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

Abstract:
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.

WebCast Link: https://usc.zoom.us/j/3154287574?pwd=c2ZjeExXR2NpVEtvZjJBd0QrRGVRZz09

Audiences: Everyone Is Invited

Contact: Lizsl De Leon