Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition

Jin, Chenfei; Xie, Jiaheng; Zhang, Siqi; Zhang, Zijing; Zhao, Yuan

doi:10.1364/oe.26.020089

Cited by 26 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Collecting the third bounce echo scattered from the hidden object allows the detection and identification of hidden scene by advanced 3D reconstruction algorithms [12]. Past results have demonstrated how this technique can be used for tracking a moving hidden object even over large distances [13,14] and for the retrieval of the 3D shape of a static hidden object by using back-projection imaging algorithms [12,15] or ellipsoid mode decomposition for multiple hidden objects [16]. Alternative methods aimed at simplifying or increasing the speed of LIDAR-like NLOS imaging rely on 2D continuous illumination [17], deep learning [18] and confocal illumination/collection [19].…”

mentioning

confidence: 99%

Non-Line-of-Sight Three-Dimensional Imaging with a Single-Pixel Camera

et al. 2019

View full text Add to dashboard Cite

Real time, high resolution 3D reconstruction of scenes hidden from the direct field of view is a challenging field of research with applications in real-life situations related e.g. to surveillance, self-driving cars and rescue missions. Most current techniques recover the 3D structure of a non-lineof-sight (NLOS) static scene by detecting the return signal from the hidden object on a scattering observation area. Here, we demonstrate the full colour retrieval of the 3D shape of a hidden scene by coupling back-projection imaging algorithms with the high-resolution time-of-flight information provided by a single-pixel camera. By using a high e ciency Single-Photon Avalanche Diode (SPAD) detector, this technique provides the advantage of imaging with no mechanical scanning parts, with acquisition times down to sub-seconds.

show abstract

mentioning

confidence: 99%

Non-Line-of-Sight Three-Dimensional Imaging with a Single-Pixel Camera

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In ToF approaches, the depth information is estimated by measuring the time needed by light to travel from the scene to the sensor [11]. Many recent imaging approaches, ranging from sparse-photon imaging [12][13][14] to non-line-of-sight (NLOS) imaging [15][16][17][18][19][20], also rely on computational techniques for enhancing imaging capabilities. Among the various possible computational imaging algorithms [21], machine learning (ML) [22] and, in particular, deep learning [23], provides a statistical or data-driven model for enhanced image retrieval [24].…”

Section: Introductionmentioning

confidence: 99%

Spatial images from temporal data

Turpin

Musarra

Kapitany

et al. 2020

Optica

View full text Add to dashboard Cite

Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial structure in the detector or illumination, i.e., imaging with just a single-point sensor, would require solving a very strongly ill-posed inverse retrieval problem that to date has not been solved. Here, we demonstrate a data-driven approach in which full 3D information is obtained with just a single-point, single-photon avalanche diode that records the arrival time of photons reflected from a scene that is illuminated with short pulses of light. Imaging with single-point time-of-flight (temporal) data opens new routes in terms of speed, size, and functionality. As an example, we show how the training based on an optical time-of-flight camera enables a compact radio-frequency impulse radio detection and ranging transceiver to provide 3D images. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

show abstract

“…They first proposed NLOS detection based on time of flight (TOF). Ellipsoid mode decomposition and fast back-projection algorithm have been presented to optimize the back-projection algorithm [3], [4]. For selecting features, Laplacian of Gaussian (LoG) and Difference of Gaussian (DoG) have been applied on the voxel space to enhance the contrast [5].…”

Section: Introductionmentioning

confidence: 99%

Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight

et al. 2020

View full text Add to dashboard Cite

Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. The simplicity, high efficiency, and automation of this algorithm make it applicable for tracking a hidden moving object.

show abstract

Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition

Cited by 26 publications

References 30 publications

Non-Line-of-Sight Three-Dimensional Imaging with a Single-Pixel Camera

Non-Line-of-Sight Three-Dimensional Imaging with a Single-Pixel Camera

Spatial images from temporal data

Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight

Contact Info

Product

Resources

About