Multi-scale Iterative Model-guided Unfolding Network for NLOS Reconstruction

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dc.contributor.authorSu, Xiongfeien_US
dc.contributor.authorHong, Yuen_US
dc.contributor.authorYe, Juntianen_US
dc.contributor.authorXu, Feihuen_US
dc.contributor.authorYuan, Xinen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:34:59Z
dc.date.available2023-10-09T07:34:59Z
dc.date.issued2023
dc.description.abstractNon-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing diffuse reflection of relay surfaces, and is potentially used in autonomous driving, medical imaging and national defense. Despite the challenges of low signal-to-noise ratio (SNR) and ill-conditioned problem, NLOS imaging has developed rapidly in recent years. While deep neural networks have achieved impressive success in NLOS imaging, most of them lack flexibility when dealing with multiple spatial-temporal resolution and multi-scene images in practical applications. To bridge the gap between learning methods and physical priors, we present a novel end-to-end Multi-scale Iterative Model-guided Unfolding (MIMU), with superior performance and strong flexibility. Furthermore, we overcome the lack of real training data with a general architecture that can be trained in simulation. Unlike existing encoder-decoder architectures and generative adversarial networks, the proposed method allows for only one trained model adaptive for various dimensions, such as various sampling time resolution, various spatial resolution and multiple channels for colorful scenes. Simulation and real-data experiments verify that the proposed method achieves better reconstruction results both in quality and quantity than existing methods.en_US
dc.description.number7
dc.description.sectionheadersImaging
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14958
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14958
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14958
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Computational photography
dc.subjectComputing methodologies
dc.subjectComputational photography
dc.titleMulti-scale Iterative Model-guided Unfolding Network for NLOS Reconstructionen_US
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