报告题目：Non-line-of-sight Reconstruction with Signal-object Collaborative Regularization

报 告 人：邱凌云，清华大学丘成桐数学科学中心助理教授

报告摘要：Non-line-of-sight imaging aims at recovering obscured objects from multiple scattered light. It has recently received widespread attention due to its potential applications such as autonomous driving, rescue operations, and remote sensing. However, in cases with high measurement noise, obtaining high-quality reconstructions remains a challenging task. In this work, we establish a unified regularization framework, which can be tailored for different scenarios, including indoor and outdoor scenes with substantial background noise under both confocal and non-confocal settings. The proposed regularization framework incorporates sparseness and non-local self-similarity of the hidden objects as well as smoothness of the measured signals. We show that the estimated signals, albedo, and surface normal of the hidden objects can be estimated reconstructed robustly even with high measurement noise under the proposed framework. Reconstruction results on synthetic and experimental data show that our approach recovers the hidden objects faithfully and outperforms state-of-the-art reconstruction algorithms in terms of both quantitative criteria and visual quality.

报告人简介：邱凌云博士，国家级高层次青年人才计划入选者，现任清华大学丘成桐数学科学中心助理教授，于2013年在美国普渡大学数学系获得博士学位。在加入清华大学之前，其曾在2015年至2018年就职于PGS (Petroleum Geo-Services)位于美国休斯敦的全球研发总部，从事地震波反演问题的研究工作。2013年至2015年，邱凌云博士在明尼苏达大学的IMA(Institute for Mathematics and its Applications)和埃克森美孚位于美国新泽西州的研究与工程中心(ExxonMobil’s Research and Engineering Technology Center)担任联合职位博士后。邱博士的主要研究兴趣包括非线性反问题的分析与计算、最优输运理论、正则化方法、最优化问题的迭代算法以及深度学习在反问题上的应用。

报告邀请人：科学与工程计算研究团队

报告时间：2021年9月16日14:30-15:30

报告地点：腾讯会议ID: 996 394 421