# LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view

arXiv: https://arxiv.org/abs/2101.10772 · HTML: https://mlsanity.com/paper/2101.10772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Seeing the World in a Bag of Chips](https://mlsanity.com/paper/2001.04642/) (2020)
- [Deep Multi-class Adversarial Specularity Removal](https://mlsanity.com/paper/1904.02672/) (2019)

## Abstract (arXiv)

Specular highlights are commonplace in images, however, methods for detecting them and in turn removing the phenomenon are particularly challenging. A reason for this, is due to the difficulty of creating a dataset for training or evaluation, as in the real-world we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high quality architectural scenes, where each scene is rendered with multiple views. In total we have 2,603 views with an average of 145 views per scene. Additionally we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3.6% in two orders of magnitude less time on our dataset.