# Illuminant and light direction estimation using Wasserstein distance method

arXiv: https://arxiv.org/abs/2503.05802 · HTML: https://mlsanity.com/paper/2503.05802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [The Z-Gromov-Wasserstein Distance](https://mlsanity.com/paper/2408.08233/) (2024)
- [Private Wasserstein Distance](https://mlsanity.com/paper/2404.06787/) (2024)
- [Numerical computation of generalized Wasserstein distances with applications to traffic model analysis](https://mlsanity.com/paper/2410.11441/) (2024)

## Abstract (arXiv)

Illumination estimation remains a pivotal challenge in image processing, particularly for robotics, where robust environmental perception is essential under varying lighting conditions. Traditional approaches, such as RGB histograms and GIST descriptors, often fail in complex scenarios due to their sensitivity to illumination changes. This study introduces a novel method utilizing the Wasserstein distance, rooted in optimal transport theory, to estimate illuminant and light direction in images. Experiments on diverse images indoor scenes, black-and-white photographs, and night images demonstrate the method's efficacy in detecting dominant light sources and estimating their directions, outperforming traditional statistical methods in complex lighting environments. The approach shows promise for applications in light source localization, image quality assessment, and object detection enhancement. Future research may explore adaptive thresholding and integrate gradient analysis to enhance accuracy, offering a scalable solution for real-world illumination challenges in robotics and beyond.