# Tolerance-Aware Deep Optics

arXiv: https://arxiv.org/abs/2502.04719 · HTML: https://mlsanity.com/paper/2502.04719/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems](https://mlsanity.com/paper/2506.23173/) (2025)
- [Low Latency Gaze Tracking via Latent Optical Sensing](https://mlsanity.com/paper/2605.17990/) (2026)
- [Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation](https://mlsanity.com/paper/2606.24096/) (2026)

## Abstract (arXiv)

Deep optics has emerged as a promising approach by co-designing optical elements with deep learning algorithms. However, current research typically overlooks the analysis and optimization of manufacturing and assembly tolerances. This oversight creates a significant performance gap between designed and fabricated optical systems. To address this challenge, we present the first end-to-end tolerance-aware optimization framework that incorporates multiple tolerance types into the deep optics design pipeline. Our method combines physics-informed modelling with data-driven training to enhance optical design by accounting for and compensating for structural deviations in manufacturing and assembly. We validate our approach through computational imaging applications, demonstrating results in both simulations and real-world experiments. We further examine how our proposed solution improves the robustness of optical systems and vision algorithms against tolerances through qualitative and quantitative analyses. Code and additional visual results are available at openimaginglab.github.io/LensTolerance.