Effective Very-wide-area 3D ToF Sensing

Paredes, Alvaro Lopez; Conde, Miguel Heredia; Loffeld, Otmar

doi:10.1109/sensors47087.2021.9639585

Cited by 3 publications

(10 citation statements)

References 26 publications

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“…Fig. 1 shows the proposed sensing methodology of our PB-ToF prototype [7]. The camera rotates around its vertical axis, ⃗ y 2 , to increase the angular range up to eventually 360 • , and drastically reduces the exposure time to avoid the introduction of motion artifacts.…”

Section: A Tof Camera Setupmentioning

confidence: 99%

“…Following the notation in [7], three different coordinate systems are considered in the camera model.…”

Section: A Tof Camera Setupmentioning

confidence: 99%

“…We present the estimation of the density accounting for the IRF of the ToF sensors [59] and with a grid discretization of N steps = 12 sub-steps in Table I. In addition, we perform a quantitative evaluation of the computational cost with respect to other ℓ 1 -minimization schemes [63], as shown in Table II Previously, we performed various simulations using random and pseudo-random binary matrices as sensing matrices to validate the applicability of the proposed sensing and recovery strategies [7]. In this paper, we propose a deterministic approach based on the PEG [6], [9] algorithm for the construction of the sensing matrices to reduce the intrinsic variability in random and pseudo-random construction methodologies.…”

Section: B Tof Sensing Schemementioning

confidence: 99%

“…We will include this valuable information into our sensing and signal recovery processes. In this paper, we thoroughly describe two methodologies to solve the inverse problem, i. e., the recovery of the sparse signal from the measurements, such as Sliced Orthogonal Matching Pursuit (OMP) [7] and Zoned sparsity-aware Adaptive Progressive Edge Growth (APEG). Each of them tackles this challenge from a different but complementary perspective.…”

Section: Introductionmentioning

confidence: 99%

“…We aim to obtain the best sensing matrix, independently on the signal considered, relying on the minimization of the density and coherence. In the primary version of the algorithm [7], we generated the sensing matrices using random or pseudo-random schemes. In this work, we propose two deterministic methodologies to reduce the variability of the aforementioned schemes.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity-Aware 3-D ToF Sensing

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Section: A Tof Camera Setupmentioning

confidence: 99%

“…Following the notation in [7], three different coordinate systems are considered in the camera model.…”

Section: A Tof Camera Setupmentioning

confidence: 99%

Section: B Tof Sensing Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparsity-Aware 3-D ToF Sensing

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Performance Evaluation of State-of-the-Art High-Resolution Time-of-Flight Cameras

2023

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It has been about 20 years since the disruptive appearance of the first time-of-flight (ToF) cameras. Since then, ToF imaging has progressively evolved. Nowadays, ToF sensors have broken the barrier of the 1-megapixel resolution, and a significant number of high-resolution ToF cameras have appeared in the market. To provide a better understanding of their performance and applications, we experimentally evaluate three state-of-the-art high-resolution ToF cameras such as Azure Kinect, Helios2, and S100D, together with the solid-state LiDAR L515. We perform various experiments to examine some key parameters, such as warmup times, accuracy, precision, lateral and axial resolutions, edge noise, unsteady scenes, and modulated waveform and optical power. Our evaluation draws various conclusions: S100D shows fluctuations within 1 mm after being powered up while the others require warm-up times. Azure Kinect, Helios2, and L515 can achieve precision within 2 mm in a measuring range of 0.5-3 m. Helios2 and S100D are more severely affected by dynamic scenes. Finally, the point clouds (PCs) generated for a white panel at a distance of 1.5 m show that flying pixels are present in all cameras, being this problem less acute for the L515.

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CS-ToF sensing by means of greedy bi-lateral fusion and near-to-optimal low-density codes

Paredes

Conde

Loffeld

2022

2022 30th European Signal Processing Conference (EUSIPCO)

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In this work, we propose a sensing scheme for the reconstruction of a highly sparse 3D view by a Pulse-Based Time-of-Flight (PB-ToF) camera aiming at achieving high angular resolution at interactive rates. The construction of the sensing matrices is focused on the optimization of the coherence and the preservation of the low density which characterize Low-Density Parity-Check (LDPC) codes. We investigate the possibility of shifting the custom sequences generated at the pixel level by selecting the shifts which maximize the minimum distance between adjacent columns, as well as the use of the information from the two complementary integration channels or taps the ToF sensor consists of. The signal reconstruction algorithm, coined greedy bi-lateral fusion, firstly determines a preliminary target probability distribution, and then re-weights it by exploiting the local correlations within the pixel array by applying a bi-lateral filtering which accounts for the affinities in spatial and intensity domains. The algorithm improves the accuracy of our camera under presence of strong noise and still preserves the speed and simplicity associated to classical greedy algorithms.

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Effective Very-wide-area 3D ToF Sensing

Cited by 3 publications

References 26 publications

Sparsity-Aware 3-D ToF Sensing

Sparsity-Aware 3-D ToF Sensing

Performance Evaluation of State-of-the-Art High-Resolution Time-of-Flight Cameras

CS-ToF sensing by means of greedy bi-lateral fusion and near-to-optimal low-density codes

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