# Deep Neural Networks for Computational Optical Form Measurements

arXiv: https://arxiv.org/abs/2007.00319 · HTML: https://mlsanity.com/paper/2007.00319/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements](https://mlsanity.com/paper/2103.01259/) (2021)
- [A framework for benchmarking uncertainty in deep regression](https://mlsanity.com/paper/2109.09048/) (2021)

## Abstract (arXiv)

Deep neural networks have been successfully applied in many different fields like computational imaging, medical healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine learning approach is explored to solve an inverse problem in the accurate measurement of optical surfaces. The approach is developed and tested using virtual measurements with known ground truth.