Fast 3D shape measurement using Fourier transform profilometry without phase unwrapping

Song, Kechen; Hu, Shaopeng; Wen, Xin; Yan, Yunhui

doi:10.1016/j.optlaseng.2016.04.003

Cited by 45 publications

(9 citation statements)

References 13 publications

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“…However, the projected texture is never decoded, hence all such methods are not true SL methods. Approaches that both project and decode a SL pattern are also plenty: a fringe SL pattern is used in [24]- [28], Gray code in [29], and line patterns in [30], [31]. More specifically: in [24]- [26] a stereo-pair is used to directly unwrap a single wrapped phase; in [27], [28], [31] a decoded SL pattern is used to drive the stereo-matching algorithm; in [29] the projected Gray code is used to directly establish the correspondences between the two cameras of a stereo-pair; and in [30] a stereo-pair is used to determine line order and drive SL decoding.…”

Section: Introductionmentioning

confidence: 99%

Multiprojector Multicamera Structured Light Surface Scanner

Petković

Pribanić

2022

IEEE Access

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Structured light surface scanners are usually limited to a single projector and cannot provide a full surface profile in a single scan. Multiple projectors must be employed to extend the imaging area over the whole surface of the imaged object. Using multiple projectors is a challenging problem due to inter-projector interference which make pattern decoding hard. We describe a practical approach to multi-projector structured light surface scanning based on sinusoidal fringe patterns and temporal phase shifting which allows simultaneous projector employment. In our approach a different set of temporal phase shifts is assigned to each projector to enable efficient and robust separation of observed combined patterns into the contributions of each projector. The proposed approach does not place any restrictions on projector placement nor on the upper limit of the number of projectors used. We demonstrate the applicability of the proposed approach on a structured light surface scanner comprised of three projectors and six cameras.INDEX TERMS structured light, 3D imaging, multi-projector structured light, temporal phase shifting, frequency-division multiplexing

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Section: Introductionmentioning

confidence: 99%

Multiprojector Multicamera Structured Light Surface Scanner

Petković

Pribanić

2022

IEEE Access

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“…17 With sets of training and testing diffusers, the phase information encoded through controlled speckle patterns can be leveraged to predict the outputs from previously unseen diffusers. 18 The non-exhaustive list of important applications include profilometry, 19 imaging through smoke, 20 and LIDAR that leverages multiple point cloud and time-of-flight information. 21 Additional examples of "nonlinear reservoir learning" are presented in, 22 which employs caustic patterns for original object reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders

Muminov¹,

Vuong²

2020

Preprint

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We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, "small-brain" neural networks for high-speed and low-light imaging. Our single-shot ptychographic approach exploits the coherent diffraction, compact representation, and edge enhancement of Fourier-tranformed spiral-phase gradients. With vortex spatial encoding, a small brain is trained to deconvolve images at rates 5-20 times faster than those achieved with random encoding schemes, where greater advantages are gained in the presence of noise. Once trained, the small brain reconstructs an object from intensity-only data, solving an inverse mapping without performing iterations on each image and without deep-learning schemes. With this hybrid, optical-digital, vortex Fourier encoded, small-brain scheme, we reconstruct MNIST Fashion objects illuminated with low-light flux (5 nJ/cm 2 ) at a rate of several thousand frames per second on a 15 W central processing unit, two orders of magnitude faster than convolutional neural networks.

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“…In addition, some techniques have also been proposed to enhance the performance of Fourier transform profilometry. For example, a grating π phase shifting technique can enlarge the measurable slope of height variation significantly [14]; the principal component analysis is used to extract the phase of the carrier from the first dominant component [15]; the binocular stereo vision and two image pairs, original image pairs and fringe image pairs, is used to restructure 3D shape and a coarse disparity map is employed to calculate the phase distribution [16].…”

Section: Introductionmentioning

confidence: 99%

Structured Light Three-Dimensional Measurement Based on Machine Learning

Zhong

Gao

Wang

et al. 2019

Sensors

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The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%.

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Fast 3D shape measurement using Fourier transform profilometry without phase unwrapping

Cited by 45 publications

References 13 publications

Multiprojector Multicamera Structured Light Surface Scanner

Multiprojector Multicamera Structured Light Surface Scanner

Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders

Structured Light Three-Dimensional Measurement Based on Machine Learning

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