# Physically-admissible polarimetric data augmentation for road-scene analysis

arXiv: https://arxiv.org/abs/2206.07431 · HTML: https://mlsanity.com/paper/2206.07431/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [Face Aging With Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1702.01983/) (2017)
- [Depth from a polarisation + RGB stereo pair](https://mlsanity.com/paper/1903.12061/) (2019)
- [Synthetic data generation for end-to-end thermal infrared tracking](https://mlsanity.com/paper/1806.01013/) (2018)
- [Deep Generative Models with Learnable Knowledge Constraints](https://mlsanity.com/paper/1806.09764/) (2018)
- [Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy](https://mlsanity.com/paper/1906.01529/) (2019)
- [Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning](https://mlsanity.com/paper/1910.04870/) (2019)
- [Polarimetric image augmentation](https://mlsanity.com/paper/2005.11044/) (2020)

## Built on by (most cited first, 1 of 1)

- [Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry](https://mlsanity.com/paper/2411.07918/) (2024)

## Abstract (arXiv)

Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis. However, its robustness may be questioned because of the small size of the training datasets. Though the issue could be solved by data augmentation, polarization modalities are subject to physical feasibility constraints unaddressed by classical data augmentation techniques. To address this issue, we propose to use CycleGAN, an image translation technique based on deep generative models that solely relies on unpaired data, to transfer large labeled road scene datasets to the polarimetric domain. We design several auxiliary loss terms that, alongside the CycleGAN losses, deal with the physical constraints of polarimetric images. The efficiency of this solution is demonstrated on road scene object detection tasks where generated realistic polarimetric images allow to improve performances on cars and pedestrian detection up to 9%. The resulting constrained CycleGAN is publicly released, allowing anyone to generate their own polarimetric images.