# Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics

arXiv: https://arxiv.org/abs/2101.00814 · HTML: https://mlsanity.com/paper/2101.00814/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [Thingi10K: A Dataset of 10,000 3D-Printing Models](https://mlsanity.com/paper/1605.04797/) (2016)
- [Fringe pattern analysis using deep learning](https://mlsanity.com/paper/1807.02757/) (2018)
- [Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network](https://mlsanity.com/paper/1901.00361/) (2019)

## Built on by (most cited first, 5 of 5)

- [Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review](https://mlsanity.com/paper/2312.16174/) (2023)
- [Motion-induced error reduction for high-speed dynamic digital fringe projection system](https://mlsanity.com/paper/2401.15938/) (2024)
- [Calibration of 3D Single-pixel Imaging Systems with a Calibration Field](https://mlsanity.com/paper/2410.07545/) (2024)
- [Comprehensive Machine Learning Benchmarking for Fringe Projection Profilometry with Photorealistic Synthetic Data](https://mlsanity.com/paper/2601.08900/) (2026)
- [Self-Supervised Dual-Frequency Phase Decomposition for Single-Shot Composite Fringe Projection Profilometry](https://mlsanity.com/paper/2606.21027/) (2026)

## Abstract (arXiv)

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications.