Paper Info
Title
Passive Non-Line-of-Sight Imaging Using Optimal Transport
Abstract
Passive non-line-of-sight (NLOS) imaging has drawn great attention in recent years. However, all existing methods are in common limited to simple hidden scenes, low-quality reconstruction, and small-scale datasets. In this paper, we propose NLOS-OT, a novel passive NLOS imaging framework based on manifold embedding and optimal transport, to reconstruct high-quality complicated hidden scenes. NLOS-OT converts the high-dimensional reconstruction task to a low-dimensional manifold mapping through optimal transport, alleviating the ill-posedness in passive NLOS imaging. Besides, we create the first large-scale passive NLOS imaging dataset, NLOS-Passive, which includes 50 groups and more than 3,200,000 images. NLOS-Passive collects target images with different distributions and their corresponding observed projections under various conditions, which can be used to evaluate the performance of passive NLOS imaging algorithms. It is shown that the proposed NLOS-OT framework achieves much better performance than the state-of-the-art methods on NLOS-Passive. We believe that the NLOS-OT framework together with the NLOS-Passive dataset is a big step and can inspire many ideas towards the development of learning-based passive NLOS imaging. Codes and dataset are publicly available ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ruixv/NLOS-OT</uri> ).
Year
DOI
Venue
2022
10.1109/TIP.2021.3128312
IEEE Transactions on Image Processing
Keywords
DocType
Volume
Non-line-of-sight imaging,optimal transport,autoencoder,manifold embedding
Journal
31
Issue
ISSN
Citations 
1
1057-7149
1
PageRank 
References 
Authors
0.39
11
7
Name
Order
Citations
PageRank
Ruixu Geng110.39
Yang Hu23817.61
Zhi Lu331.43
Cong Yu410.39
Houqiang Li52090172.30
Hengyu Zhang610.39
Yan Chen73842220.64