TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns
Yi Cai,
Mingyu Guo,
Congying Wang,
Xiaowei Lu,
Xuanke Zeng,
Yiling Sun,
Yuexia Ai,
Shixiang Xu,
Jingzhen Li
Affiliations
Yi Cai
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Mingyu Guo
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Congying Wang
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Xiaowei Lu
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Xuanke Zeng
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yiling Sun
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yuexia Ai
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Shixiang Xu
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Jingzhen Li
Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer’s benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.
