3D Imaging from Multipath Temporal Echoes

Turpin, Alex; Kapitany, Valentin; Radford, Jack; Rovelli, Davide; Mitchell, Kevin J.; Lyons, Ashley; Starshynov, Ilya; Faccio, Daniele

doi:10.1103/physrevlett.126.174301

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…In fact, many practical scenarios already operate inside enclosed reverberant environments, usually without realizing the implications thereof regarding the achievable resolution. Examples include indoor localization 183,[196][197][198][199][200] and imaging, 201 biomedical breast imaging inside (reconfigurable) metallic enclosures, 202 impact localization with elastic waves propagating on solid surfaces, 203,204 or photo-acoustic imaging inside an acoustically reverberant cavity. 205 Many applications of EM meta-imaging indeed involve operation in indoor environments which naturally act as RCAs, many of which will soon be endowed with programmability as part of next-generation wireless communication paradigms.…”

Section: B Meta-programmable Reverberation-coded Aperturesmentioning

confidence: 99%

Intelligent meta-imagers: From compressed to learned sensing

Saigre-Tardif

Faqiri

Zhao

et al. 2022

Applied Physics Reviews

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Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of “intelligence” in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step “over-the-air.” We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.

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Section: B Meta-programmable Reverberation-coded Aperturesmentioning

confidence: 99%

Intelligent meta-imagers: From compressed to learned sensing

Saigre-Tardif

Faqiri

Zhao

et al. 2022

Applied Physics Reviews

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“…In contrast, given the complex geometry of our indoor environment, an analytical description of the Green's function is intractable. Instead, our AI-based reconstruction algorithm implicitly learns the Green's functions from the training data, in some ways similar to refs [ 21 , 41 ]. Specifically, to obtain a large labeled data set to train our network with supervised learning, we use a set of stereo optical cameras that are embedded in the environment and synchronized with the I2MR during training.…”

Section: Centralized Acquisition Of Context Awarenessmentioning

confidence: 99%

Intelligent indoor metasurface robotics

Zhao

Zhang

et al. 2022

National Science Review

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Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their abilities to perceive and respond to dynamically evolving complex indoor environment because of their inherently limited sensing and computing resources that are moreover traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics (I2MR), where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR’s limbs (motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold the microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR’s brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR’s brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement experimentally a proof-of-principle demonstration at around 2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

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“…However, the high costs and complex structures associated with these approaches hinder the progress of single-pixel imaging. Consequently, alternative methods that do not involve scanning or unique lighting structures have been proposed in recent years [30][31][32] . These methods, known as 3D imaging from temporal data, capture the temporal information (ToF) of the entire scene using a single-pixel single-photon detector (SPD) and a time digital converter (TDC), followed by reconstruction of the 3D images using an artificial neural network (ANN) [33][34][35][36] .…”

Section: Introductionmentioning

confidence: 99%

Single-pixel 3D imaging based on fusion temporal data of single-photon detector and millimeter-wave radar

Lai,

Liang,

Zhu

et al. 2024

Chin. Opt. Lett.

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Recently, there has been increased attention toward 3D imaging using single-pixel single-photon detection (also known as temporal data) due to its potential advantages in terms of cost and power efficiency. However, to eliminate the symmetry blur in the reconstructed images, a fixed background is required. This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives. Subsequently, the 3D information can be reconstructed from the one-dimensional fusion temporal data by using an artificial neural network. Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.

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3D Imaging from Multipath Temporal Echoes

Cited by 20 publications

References 23 publications

Intelligent meta-imagers: From compressed to learned sensing

Intelligent meta-imagers: From compressed to learned sensing

Intelligent indoor metasurface robotics

Single-pixel 3D imaging based on fusion temporal data of single-photon detector and millimeter-wave radar

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