# PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

arXiv: https://arxiv.org/abs/2409.12659 · HTML: https://mlsanity.com/paper/2409.12659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [PANDORA: Polarization-Aided Neural Decomposition Of Radiance](https://mlsanity.com/paper/2203.13458/) (2022)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [Polarimetric Pose Prediction](https://mlsanity.com/paper/2112.03810/) (2021)

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

- [Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments](https://mlsanity.com/paper/2509.24731/) (2025)
- [Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture](https://mlsanity.com/paper/2605.02529/) (2026)
- [CANSURF: An ASV-View Can Dataset and Benchmark for Detection and Tracking of Surface-Level Debris](https://mlsanity.com/paper/2605.16774/) (2026)

## Abstract (arXiv)

Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024.