Chengyang Liu

Distilling Quasi-Conformal Mapping: A Generalizable and Efficient Solution for Wide-Angle Correction

This paper introduces a novel framework for wide-angle correction by distilling the geometric principles of quasi-conformal (QC) mapping into a generalizable and efficient deep neural network. Our methodology can be divided into two primary stages. In the first stage, we develop an annotation-free t