Work

Deep Learning Enhanced Dynamic 3-Dimensional Shape Measurement Using Single-Shot Spatial Multiplexing and Transformer-Based Phase Retrieval

Year: 2025

Type: article

Abstract: Real-time, high-precision 3-dimensional (3D) imaging is essential for applications such as industrial inspection, robotic navigation, and human–computer interaction. Fringe projection profilometry (FPP), a widely used structured light method, achieves high spatiotemporal resolution by rapidly projecting and processing of fringe patterns. However, traditional multi-frame FPP methods are hindered by motion-induced artifacts and computational bottlenecks, limiting their applicability in dynamic environments. In this work, we propose a multiplexed structured light 3D measurement method that integrates a physics-based Transformer framework to minimize the number of projection patterns required for precise single-snapshot measurements. This method extracts accurate phase information from a single fringe image, enabling artifact-free, high-resolution 3D surface reconstruction. By combining low-frequency triangular waves with high-frequency sinusoidal fringes, we ensure unambiguous phase retrieval, providing the deep neural network with reliable inputs. The Transformer-based network leverages superior global information capture and multi-scale feature learning capabilities for robust fringe analysis and phase unwrapping, thereby enhancing the accuracy and generalization of depth prediction. Experimental evaluations demonstrate that our method outperforms traditional single-frame phase retrieval techniques and other deep learning-based methods in terms of precision and robustness. Dynamic measurements of complex objects with various materials further validate its potential for high-speed, real-time 3D imaging in intelligent manufacturing and augmented reality.

Source: Advanced Devices & Instrumentation

DOI: 10.34133/adi.0095

Authors: Yixuan Li, Yile Xiao, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

Institution: Nanjing University of Science and Technology

Language: English

Topic: Optical measurement and interference techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense, “333 Project” of Jiangsu Province, Fundamental Research Funds for the Central Universities

Awards: G2867457233, G591533327, G655483996, G695489505