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

Yahong Feng,

Shijin Zhang,

Yuqiang Wu

и другие.

Опубликована: Дек. 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.

Язык: Английский

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

Junjie Cui,

Dong Chen, Xunren Li

и другие.

Optics and Lasers in Engineering, Год журнала: 2025, Номер 187, С. 108873 - 108873

Опубликована: Фев. 11, 2025

Язык: Английский

Процитировано

1

Enhancing error correction in fringe projection: An innovative method for acquisition-side errors DOI
Haoyue Liu, Lei Liu, Changshuai Fang

и другие.

Optics and Lasers in Engineering, Год журнала: 2025, Номер 186, С. 108815 - 108815

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

0

Single-Model Self-Recovering Fringe Projection Profilometry Absolute Phase Recovery Method Based on Deep Learning DOI Creative Commons
Xu Li, Yuxia Shen, Qifu Meng

и другие.

Sensors, Год журнала: 2025, Номер 25(5), С. 1532 - 1532

Опубликована: Март 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.

Язык: Английский

Процитировано

0

A parameter optimization strategy of the UAOM device for high dynamic range 3D shape measurement DOI
Yifan Chen, Zefeng Sun, Luyuan Feng

и другие.

Optics & Laser Technology, Год журнала: 2025, Номер 186, С. 112749 - 112749

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

0

Single Fringe Phase Retrieval for Translucent Object Measurements Using a Deep Convolutional Generative Adversarial Network DOI Creative Commons
Jiayan He, Yixuan Huang,

Juhao Wu

и другие.

Sensors, Год журнала: 2025, Номер 25(6), С. 1823 - 1823

Опубликована: Март 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

Язык: Английский

Процитировано

0

Phase feedback fringe projection profilometry for shiny objects DOI
Rigoberto Juarez-Salazar, Fabio Vega, Sofia Esquivel-Hernandez

и другие.

Optics and Lasers in Engineering, Год журнала: 2025, Номер 191, С. 109013 - 109013

Опубликована: Апрель 10, 2025

Язык: Английский

Процитировано

0

Assessment of Woody Breast in Broiler Breast Fillets Using Sinusoidal Illumination Reflectance Imaging Coupled with Surface Profilometry DOI
Jiaxu Cai, Yuzhen Lu

Journal of Food Engineering, Год журнала: 2024, Номер unknown, С. 112459 - 112459

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1

Deep diffusion learning of mutual-reflective structured light patterns for multi-body three-dimensional imaging DOI Creative Commons
Lei Lü,

Yuejiao Guo,

Zhilong Su

и другие.

Optics Express, Год журнала: 2024, Номер 32(21), С. 36171 - 36171

Опубликована: Сен. 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.

Язык: Английский

Процитировано

1

Novel approach for fast structured light framework using deep learning DOI

Won-Hoe Kim,

Bongjoong Kim,

Hyung‐gun Chi

и другие.

Image and Vision Computing, Год журнала: 2024, Номер 150, С. 105204 - 105204

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

0

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

Long Shu,

Junxiang Li, Yijun Du

и другие.

Photonics, Год журнала: 2024, Номер 11(11), С. 994 - 994

Опубликована: Окт. 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.

Язык: Английский

Процитировано

0