Passive Non-Line-of-Sight Imaging Using Optimal Transport

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 (https://github.com/ruixv/NLOS-OT).

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:31

Enthalten in:

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society - 31(2022) vom: 22., Seite 110-124

Sprache:

Englisch

Beteiligte Personen:

Geng, Ruixu [VerfasserIn]
Hu, Yang [VerfasserIn]
Lu, Zhi [VerfasserIn]
Yu, Cong [VerfasserIn]
Li, Houqiang [VerfasserIn]
Zhang, Hengyu [VerfasserIn]
Chen, Yan [VerfasserIn]

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Journal Article

Anmerkungen:

Date Revised 01.12.2021

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1109/TIP.2021.3128312

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM333451457