Log in

Relevant bibliographies by topics / Non-line-of-sight-imaging

Contents

Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'Non-line-of-sight-imaging'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Non-line-of-sight-imaging.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Non-line-of-sight-imaging"

1

Faccio, Daniele, Andreas Velten, and Gordon Wetzstein. "Non-line-of-sight imaging." Nature Reviews Physics 2, no. 6 (May 13, 2020): 318–27. http://dx.doi.org/10.1038/s42254-020-0174-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Faccio, Daniele. "Non-Line-of-Sight Imaging." Optics and Photonics News 30, no. 1 (January 1, 2019): 36. http://dx.doi.org/10.1364/opn.30.1.000036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Tingyi Yu, 于亭义, 乔木 Mu Qiao, 刘红林 Honglin Liu, and 韩申生 Shensheng Han. "Non-Line-of-Sight Imaging Through Deep Learning." Acta Optica Sinica 39, no. 7 (2019): 0711002. http://dx.doi.org/10.3788/aos201939.0711002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Beckus, Andre, Alexandru Tamasan, and George K. Atia. "Multi-Modal Non-Line-of-Sight Passive Imaging." IEEE Transactions on Image Processing 28, no. 7 (July 2019): 3372–82. http://dx.doi.org/10.1109/tip.2019.2896517.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Wu, Cheng, Jianjiang Liu, Xin Huang, Zheng-Ping Li, Chao Yu, Jun-Tian Ye, Jun Zhang, et al. "Non–line-of-sight imaging over 1.43 km." Proceedings of the National Academy of Sciences 118, no. 10 (March 3, 2021): e2024468118. http://dx.doi.org/10.1073/pnas.2024468118.

Full text

Abstract:

Non–line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial–temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial–temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.

APA, Harvard, Vancouver, ISO, and other styles

6

La Manna, Marco, Fiona Kine, Eric Breitbach, Jonathan Jackson, Talha Sultan, and Andreas Velten. "Error Backprojection Algorithms for Non-Line-of-Sight Imaging." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 7 (July 1, 2019): 1615–26. http://dx.doi.org/10.1109/tpami.2018.2843363.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

La Manna, Marco, Ji-Hyun Nam, Syed Azer Reza, and Andreas Velten. "Non-line-of-sight-imaging using dynamic relay surfaces." Optics Express 28, no. 4 (February 12, 2020): 5331. http://dx.doi.org/10.1364/oe.383586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Klein, Jonathan, Martin Laurenzis, Matthias B. Hullin, and Julian Iseringhausen. "Calibration scheme for non-line-of-sight imaging setups." Optics Express 28, no. 19 (September 9, 2020): 28324. http://dx.doi.org/10.1364/oe.398647.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Lin, Di, Connor Hashemi, and James R. Leger. "Passive non-line-of-sight imaging using plenoptic information." Journal of the Optical Society of America A 37, no. 4 (March 11, 2020): 540. http://dx.doi.org/10.1364/josaa.377821.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Thrampoulidis, Christos, Gal Shulkind, Feihu Xu, William T. Freeman, Jeffrey H. Shapiro, Antonio Torralba, Franco N. C. Wong, and Gregory W. Wornell. "Exploiting Occlusion in Non-Line-of-Sight Active Imaging." IEEE Transactions on Computational Imaging 4, no. 3 (September 2018): 419–31. http://dx.doi.org/10.1109/tci.2018.2829599.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Non-line-of-sight-imaging"

1

Klein, Jonathan [Verfasser]. "Transient Non-Line-of-Sight Imaging / Jonathan Klein." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1235524574/34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Tancik, Matthew. "Non-line-of-sight imaging using data-driven approaches." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119568.

Full text

Abstract:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-69).
Non-line-of-sight (NLOS) imaging is desirable for its many potential applications such as detecting a vehicle occluded by a building's corner or imaging through fog. Traditional NLOS imaging techniques solve an inverse problem and are limited by computational complexity and forward model accuracy. This thesis proposes the application of data-driven techniques to NLOS imaging to leverage the convolutional neural network's ability to learn invariants to scene variations. We demonstrate the classification of an object hidden behind a scattering media along with the localization and classification of an object occluded by a corner. In addition we demonstrate the use of generative neural networks to construct images from viewpoints that extend the original camera's field of view.
by Matthew Tancik.
M. Eng.

APA, Harvard, Vancouver, ISO, and other styles

3

Henley, Connor A. "Non-line-of-sight imaging using multi-bounce light." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121655.

Full text

Abstract:

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 91-93).
Non-line-of-sight (NLOS) imaging techniques produce images from light that has travelled from the scene of interest to the observer via indirect paths which typically include multiple reflections. Such techniques can be particularly useful when the direct line of sight between the observer and the scene is blocked. In this thesis we will explore two NLOS imaging techniques. The first is an occlusion-assisted imaging technique, which constructs images of hidden scenes by interpreting the patterns that are imposed on multiply reflected light by occluding objects. We will provide a conceptual and theoretical introduction to our technique, which uses a focused, scannable illumination source and a single-pixel, lensless detector. We will then present the results from an experimental implementation of this technique in a challenging environment. This will be followed by an analysis of a number of challenges that are commonly encountered in active, occlusion-assisted imaging scenarios, including single-bounce light rejection, inter-reflections, and asymmetries in measurement geometry. Finally, we will introduce a new NLOS imaging technique which uses the time-of-flight information in multiply reflected light to produce an unobstructed, line-of-sight view of a hidden scene. We will provide a conceptual introduction to the technique as well as a derivation of the physical model that underlies it, and will also discuss methods for visualizing the technique's output.
by Connor A. Henley.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

APA, Harvard, Vancouver, ISO, and other styles

4

Saunders, Charles. "Occluder-aided non-line-of-sight imaging." Thesis, 2021. https://hdl.handle.net/2144/43116.

Full text

Abstract:

Non-line-of-sight (NLOS) imaging is the inference of the properties of objects or scenes outside of the direct line-of-sight of the observer. Such inferences can range from a 2D photograph-like image of a hidden area, to determining the position, motion or number of hidden objects, to 3D reconstructions of a hidden volume. NLOS imaging has many enticing potential applications, such as leveraging the existing hardware in many automobiles to identify hidden pedestrians, vehicles or other hazards and hence plan safer trajectories. Other potential application areas include improving navigation for robots or drones by anticipating occluded hazards, peering past obstructions in medical settings, or in surveying unreachable areas in search-and-rescue operations. Most modern NLOS imaging methods fall into one of two categories: active imaging methods that have some control of the illumination of the hidden area, and passive methods that simply measure light that already exists. This thesis introduces two NLOS imaging methods, one of each category, along with modeling and data processing techniques that are more broadly applicable. The methods are linked by their use of objects (‘occluders’) that reside somewhere between the observer and the hidden scene and block some possible light paths. Computational periscopy, a passive method, can recover the unknown position of an occluding object in the hidden area and then recover an image of the hidden scene behind it. It does so using only a single photograph of a blank relay wall taken by an ordinary digital camera. We develop also a framework using an optimized preconditioning matrix to improve the speed at which these reconstructions can be made and greatly improve the robustness to ambient light. Lastly, we develop tools necessary to demonstrate recovery of scenes at multiple unknown depths – paving the way towards three-dimensional reconstructions. Edge-resolved transient imaging, an active method, enables the formation of 2.5D representations – a plan view plus heights – of large-scale scenes. A pulsed laser illuminates spots along a small semi-circle on the floor, centered on the edge of a vertical wall such as in a doorway. The wall edge occludes some light paths, only allowing the laser light reflecting off of the floor to illuminate certain portions of the hidden area beyond the wall, depending on where along the semi-circle it is illuminating. The time at which photons return following a laser pulse is recorded. The occluding wall edge provides angular resolution, and time-resolved sensing provides radial resolution. This novel acquisition strategy, along with a scene response model and reconstruction algorithm, allow for 180° field of view reconstructions of large-scale scenes unlike other active imaging methods. Lastly, we introduce a sparsity penalty named mutually exclusive group sparsity (MEGS), that can be used as a constraint or regularization in optimization problems to promote solutions in which certain components are mutually exclusive. We explore how this penalty relates to other similar penalties, develop fast algorithms to solve MEGS-regularized problems, and demonstrate how enforcing mutual exclusivity structure can provide great utility in NLOS imaging problems.

APA, Harvard, Vancouver, ISO, and other styles

5

"Terahertz Holography for Non-line of Sight Imaging." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55514.

Full text

Abstract:

abstract: The objective of this work is to design a novel method for imaging targets and scenes which are not directly visible to the observer. The unique scattering properties of terahertz (THz) waves can turn most building surfaces into mirrors, thus allowing someone to see around corners and various occlusions. In the visible regime, most surfaces are very rough compared to the wavelength. As a result, the spatial coherency of reflected signals is lost, and the geometry of the objects where the light bounced on cannot be retrieved. Interestingly, the roughness of most surfaces is comparable to the wavelengths at lower frequencies (100 GHz – 10 THz) without significantly disturbing the wavefront of the scattered signals, behaving approximately as mirrors. Additionally, this electrically small roughness is beneficial because it can be used by the THz imaging system to locate the pose (location and orientation) of the mirror surfaces, thus enabling the reconstruction of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects. Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments. The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2019

APA, Harvard, Vancouver, ISO, and other styles

6

"Adaptive Lighting for Data-Driven Non-Line-Of-Sight 3D Localization." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53639.

Full text

Abstract:

abstract: Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumina- tion source is a challenging task with vital applications including surveillance and robotics. Recent NLOS reconstruction advances have been achieved using time-resolved measure- ments. Acquiring these time-resolved measurements requires expensive and specialized detectors and laser sources. In work proposes a data-driven approach for NLOS 3D local- ization requiring only a conventional camera and projector. The localisation is performed using a voxelisation and a regression problem. Accuracy of greater than 90% is achieved in localizing a NLOS object to a 5cm × 5cm × 5cm volume in real data. By adopting the regression approach an object of width 10cm to localised to approximately 1.5cm. To generalize to line-of-sight (LOS) scenes with non-planar surfaces, an adaptive lighting al- gorithm is adopted. This algorithm, based on radiosity, identifies and illuminates scene patches in the LOS which most contribute to the NLOS light paths, and can factor in sys- tem power constraints. Improvements ranging from 6%-15% in accuracy with a non-planar LOS wall using adaptive lighting is reported, demonstrating the advantage of combining the physics of light transport with active illumination for data-driven NLOS imaging.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2019

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Non-line-of-sight-imaging"

1

Isogawa, Mariko, Dorian Chan, Ye Yuan, Kris Kitani, and Matthew O’Toole. "Efficient Non-Line-of-Sight Imaging from Transient Sinograms." In Computer Vision – ECCV 2020, 193–208. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Non-line-of-sight-imaging"

1

Lindell, David B., Gordon Wetzstein, and Vladlen Koltun. "Acoustic Non-Line-Of-Sight Imaging." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00694.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Maeda, Tomohiro, Yiqin Wang, Ramesh Raskar, and Achuta Kadambi. "Thermal Non-Line-of-Sight Imaging." In 2019 IEEE International Conference on Computational Photography (ICCP). IEEE, 2019. http://dx.doi.org/10.1109/iccphot.2019.8747343.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Dave, Akshat, Muralidhar Madabhushi Balaji, Prasanna Rangarajan, Ashok Veeraraghavan, and Marc P. Christensen. "Foveated Non-line-of-sight Imaging." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/cosi.2020.cth5c.6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Tanaka, Kenichiro, Yasuhiro Mukaigawa, and Achuta Kadambi. "Polarized Non-Line-of-Sight Imaging." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00221.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

O'Toole, Matthew, David B. Lindell, and Gordon Wetzstein. "Confocal non-line-of-sight imaging." In SIGGRAPH '18: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3214745.3214795.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

La Manna, Marco, Xioachun Liu, Ji-Hyun Nam, Martin Laurenzis, and Andreas Velten. "A line-of-sight approach for non-line-of-sight imaging (Conference Presentation)." In Computational Imaging IV, edited by Jonathan C. Petruccelli, Abhijit Mahalanobis, and Lei Tian. SPIE, 2019. http://dx.doi.org/10.1117/12.2519002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Chen, Wenzheng, Simon Daneau, Colin Brosseau, and Felix Heide. "Steady-State Non-Line-Of-Sight Imaging." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00695.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

O'Toole, Matthew, David B. Lindell, and Gordon Wetzstein. "Real-time non-line-of-sight imaging." In SIGGRAPH '18: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3214907.3214920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Willomitzer, Florian, Fengqiang Li, Prasanna Rangarajan, and Oliver Cossairt. "Non-Line-of-Sight Imaging using Superheterodyne Interferometry." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cosi.2018.cm2e.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lin, Di, James R. Leger, and Connor Hashemi. "Non-Line-of-Sight Imaging using Plenoptic Information." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/cosi.2019.cm2a.5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!