Fixed-Pattern Noise Modeling and Removal in Time-of-Flight Sensing

Georgiev, Mihail; Bregović, Robert; Gotchev, Atanas

doi:10.1109/tim.2015.2494622

Cited by 25 publications

(14 citation statements)

References 17 publications

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“…Continuous-wave modulation technique is described in detail by Georgiev et al with excellent emphasis on noise modelling [13,14]. The distance resolution is about 1 cm and so spatial resolution is generally low compared to structured-light scanners.…”

Section: Time-of-flight Techniquementioning

confidence: 99%

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Vostok: 3D scanner simulation for industrial robot environments

Rossi

Barbiero

Carli

2020

ELCVIA

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Computer vision will drive the next wave of robot applications. Latest three-dimensional scanners provide increasingly realistic object reconstructions. We introduce an innovative simulator that allows interacting with those scanners within the operating environment, thus creating a powerful tool for developers, researchers and students. In particular, we present a novel approach for simulating structured-light and timeof-flight sensors. Qualitative results demonstrate the efficiency and reliability in industrial environments. By using the programmability of modern GPUs, it is now possible to make greater use of parallelized simulative approaches. Apart from the easy modification of sensor parameters, the main advantage in simulation is the opportunity of carrying out experiments under reproducible conditions, especially for dynamic scene setups. Moreover, thanks to a great computational power, it is possible to generate huge amounts of synthetic data which can be used as test datasets for training machine learning models.

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Section: Time-of-flight Techniquementioning

confidence: 99%

“…We decided to approximate the sum of all these noises with a Gaussian normal distribution. The inspiration comes from the studies [13,14] that describe a noise model for low sensing environments. In these, a ToF device works in suboptimal conditions (e.g.…”

Section: Time-of-flight Camera Simulationmentioning

confidence: 99%

Vostok: 3D scanner simulation for industrial robot environments

Rossi

Barbiero

Carli

2020

ELCVIA

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“…Previous work for correcting imagery from 3D flash LiDAR imagers has been mostly focused on image processing, filtering, or machine learning methods. [7][8][9][10][11][12][13] Some work has focused on sampling the FPA to produce a correction, though this process is tedious and requires significant effort.…”

Section: Introductionmentioning

confidence: 99%

Toward snapshot correction of 3D flash LiDAR imagers

et al. 2021

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We present methods enabling rapid non-uniformity and range walk error correction of 3D flash LiDAR imagers that exhibit electronic crosstalk caused by simultaneously triggering too many detectors. This additional electronic crosstalk is referred to as simultaneous ranging crosstalk noise (SRCN). Using a method in which the 3D flash LiDAR imager views a checkerboard target downrange, the SRCN is largely mitigated. Additionally, processing techniques for computing the non-uniformity correction (NUC) and range walk error correction are described; these include an in-situ thermally compensated dark-frame non-uniformity correction, image processing and filtering techniques for the creation of a photo-response non-uniformity correction, and characterization and correction of the range walk error using data collected across the full focal plane array without the need for sampling or windowing. These methods result in the ability to correct noisy test validation data to a range precision of 8.04 cm and a range accuracy of 1.73 cm and to improve the signal-to-noise ratio of the intensity return by 15 to 49 dB. Visualization of a 3D scene corrected by this process is additionally presented.

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“…All of these will provide the effective solution for the speed problem, due to the large array and big image data. But it also suffers from the noise deterioration [23,24], especially the Fixed Pattern Noise (FPN) because of the large layout area penalty [25][26][27][28][29][30]. The signal data is transferred to the column processor and subtracted from the previously stored reset level data to provide FPN correction, designed in [31], which will occupy the readout cycle.…”

Section: Introductionmentioning

confidence: 99%

An improved segmented DAC for column readout circuit correction of large array CMOS image sensor

Guo

2020

IEICE Electron. Express

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The non-ideal factors and error sources of segmented DAC for multi-channel large array CMOS image sensor are given. An improved precise segmented DAC using adaptive switching technology is proposed. This scheme has been verified on a 50mm×50mm large array CMOS image sensor prototype chip, which consisting of 8320× 8320 pixel array was designed and fabricated in 55nm CMOS 1P4M standard process. The measurement results show that the DNL of DAC can be reduced from 33 LSBs of traditional structure to within 0.5LSB, and the large array sensor chip reaches a high intrinsic dynamic range of 75dB, a low FPN of 0.06%, and a low photo response nonuniformity of 1.5% respectively. Finally, a good raw image is taken by the prototype sensor.

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Fixed-Pattern Noise Modeling and Removal in Time-of-Flight Sensing

Cited by 25 publications

References 17 publications

Vostok: 3D scanner simulation for industrial robot environments

Vostok: 3D scanner simulation for industrial robot environments

Toward snapshot correction of 3D flash LiDAR imagers

An improved segmented DAC for column readout circuit correction of large array CMOS image sensor

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