Adaptive high dynamic range 3D shape measurement based on time-domain superposition

Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 187, P. 108873 - 108873

Published: Feb. 11, 2025

Language: Английский

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Single-Model Self-Recovering Fringe Projection Profilometry Absolute Phase Recovery Method Based on Deep Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1532 - 1532

Published: March 1, 2025

A drawback of fringe projection profilometry (FPP) is that it still a challenge to perform efficient and accurate high-resolution absolute phase recovery with only single measurement. This paper proposes single-model self-recovering method based on deep learning. The built Fringe Prediction Self-Recovering network converts image acquired by camera into four mode images. algorithm adopted obtain wrapped phases grades, realizing from shot. Low-cost dataset preparation realized the constructed virtual measurement system. prediction showed good robustness generalization ability in experiments multiple scenarios using different lighting conditions both physical systems. recovered MAE real system was controlled be 0.015 rad, reconstructed point cloud fitting RMSE 0.02 mm. It experimentally verified proposed can achieve under complex ambient conditions. Compared existing methods, this does not need assistance additional modes process images directly. Combining learning technique simplified retrieval unwrapping, simpler more efficient, which provides reference for fast, lightweight, online detection FPP.

Language: Английский

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A parameter optimization strategy of the UAOM device for high dynamic range 3D shape measurement

Optics & Laser Technology, Journal Year: 2025, Volume and Issue: 186, P. 112749 - 112749

Published: March 10, 2025

Language: Английский

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Single Fringe Phase Retrieval for Translucent Object Measurements Using a Deep Convolutional Generative Adversarial Network

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1823 - 1823

Published: March 14, 2025

Fringe projection profilometry (FPP) is a measurement technique widely used in the field of 3D reconstruction. However, it faces issues phase shift and reduced fringe modulation depth when measuring translucent objects, leading to decreased accuracy. To reduce impact surface scattering effects on wrapped during actual measurement, we propose single-frame retrieval method named GAN-PhaseNet improve subsequent accuracy for objects. The network primarily relies generative adversarial framework, with significant enhancements implemented generator network, including integrating U-net++ architecture, Resnet101 as backbone feature extraction, multilevel attention module fully utilizing high-level features source image. results ablation comparison experiment show that proposed has superior results, not only achieving conventional objects no effect slight but also obtaining lowest errors severe compared other convolution neural networks (CDLP, Unet-Phase, DCFPP). Under varying noise levels frequencies, demonstrates excellent robustness generalization capabilities. It can be applied computational imaging techniques field, introducing new ideas

Language: Английский

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Phase feedback fringe projection profilometry for shiny objects

Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 191, P. 109013 - 109013

Published: April 10, 2025

Language: Английский

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Enhancing error correction in fringe projection: An innovative method for acquisition-side errors

Optics and Lasers in Engineering, Journal Year: 2025, Volume and Issue: 186, P. 108815 - 108815

Published: Jan. 7, 2025

Language: Английский

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Novel approach for fast structured light framework using deep learning

Image and Vision Computing, Journal Year: 2024, Volume and Issue: 150, P. 105204 - 105204

Published: Aug. 8, 2024

Language: Английский

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Deep diffusion learning of mutual-reflective structured light patterns for multi-body three-dimensional imaging

Optics Express, Journal Year: 2024, Volume and Issue: 32(21), P. 36171 - 36171

Published: Sept. 16, 2024

Simultaneous structured light imaging of multiple objects has become more demanding and widely in many scenarios involving robot operations intelligent manufacturing. However, it is challenged by pattern aliasing caused mutual reflection between high-reflective objects. To this end, we propose to learn clear fringe patterns from aliased mutual-reflective observations diffusion models for achieving high-fidelity multi-body reconstruction line with typical phase-shift algorithms. Regarding as a formation adding significant noise, build supervised generative learning framework based on then train self-attention-based deep network U-Net-like skip-connected encoder-decoder architecture. We demonstrate the generalization capability trained model recovery its performance phase three-dimensional (3D) shape reconstruction. Both experimental results show that proposed method expected feasibility accuracy, heralding promising solution addressing current challenge various 3D tasks.

Language: Английский

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Single-Shot Fringe Projection Profilometry Based on LC-SLM Modulation and Polarization Multiplexing

Photonics, Journal Year: 2024, Volume and Issue: 11(11), P. 994 - 994

Published: Oct. 22, 2024

Fringe projection profilometry (FPP) is extensively utilized for the 3D measurement of various specimens. However, traditional FPP typically requires at least three phase-shifted fringe patterns to achieve a high-quality phase map. In this study, we introduce single-shot method based on common path polarization interferometry. our method, projected pattern created through interference two orthogonal circularly polarized light beams modulated by liquid crystal spatial modulator (LC-SLM). A camera employed capture reflected pattern, enabling simultaneous acquisition four-step phase-shifting patterns. The system benefits from advanced anti-vibration capabilities attributable self-interference optical design. Furthermore, utilization low-coherence LED source results in reduced noise levels compared laser source. experimental demonstrate that proposed can yield outcomes with high accuracy and efficiency.

Language: Английский

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A deep learning-based stereo speckle image matching method for rapid 3D reconstruction of weakly-textured surfaces

Published: Dec. 5, 2024

Currently, the 3D model reconstruction technology based on binocular stereo vision becomes very popular, however, current matching method is difficult to be applied for objects with weakly-textured surface, further leads low accuracy and poor efficiency of those objects. To improve efficiency, a series methods, such as image enhancement, better feature extraction algorithm structured light have been searched. However, methods provided are either costly or computationally complex, which lead limited application. solve above problem, new has proposed in this paper, which, using speckle patterns enhance texture features surface. In addition, analysis traditional takes advantage great potential deep learning techniques efficiency. Experiments demonstrate that learning-based network achieves 10^-3 pixels 9.37×10⁵ POI/S. With method, fast accurate surface can achieved.

Language: Английский

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