# Calibration of 3D Single-pixel Imaging Systems with a Calibration Field

arXiv: https://arxiv.org/abs/2410.07545 · HTML: https://mlsanity.com/paper/2410.07545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Thingi10K: A Dataset of 10,000 3D-Printing Models](https://mlsanity.com/paper/1605.04797/) (2016)
- [Deep ChArUco: Dark ChArUco Marker Pose Estimation](https://mlsanity.com/paper/1812.03247/) (2018)
- [Fringe pattern analysis using deep learning](https://mlsanity.com/paper/1807.02757/) (2018)
- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)
- [Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics](https://mlsanity.com/paper/2101.00814/) (2021)
- [Ultra-high-speed color imaging with single-pixel detectors under low light level](https://mlsanity.com/paper/1907.09517/) (2019)
- [3D Fourier ghost imaging via semi-calibrated photometric stereo](https://mlsanity.com/paper/2112.13967/) (2021)
- [Phase-sensitive x-ray ghost imaging](https://mlsanity.com/paper/1903.08345/) (2019)
- [3D Single-pixel imaging with active sampling patterns and learning based reconstruction](https://mlsanity.com/paper/2209.02705/) (2022)

## Abstract (arXiv)

3D single-pixel imaging (SPI) is a promising imaging technique that can be ffexibly applied to various wavebands. The main challenge in 3D SPI is that the calibration usually requires a large number of standard points as references, which are tricky to capture using single-pixel detectors. Conventional solutions involve sophisticated device deployment and cumbersome operations, resulting in hundreds of images needed for calibration. In our work, we construct a Calibration Field (CaliF) to efffciently generate the standard points from one single image. A high accuracy of the CaliF is guaranteed by the technique of deep learning and digital twin. We perform experiments with our new method to verify its validity and accuracy. We believe our work holds great potential in 3D SPI systems or even general imaging systems.