# AIM 2020: Scene Relighting and Illumination Estimation Challenge

arXiv: https://arxiv.org/abs/2009.12798 · HTML: https://mlsanity.com/paper/2009.12798/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 18 of 18)

- [NTIRE 2021 Depth Guided Image Relighting Challenge](https://mlsanity.com/paper/2104.13365/) (2021)
- [S3Net: A Single Stream Structure for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00681/) (2021)
- [Multi-modal Bifurcated Network for Depth Guided Image Relighting](https://mlsanity.com/paper/2105.00690/) (2021)
- [Relighting from a Single Image: Datasets and Deep Intrinsic-based Architecture](https://mlsanity.com/paper/2409.18770/) (2024)
- [Multi-scale Self-calibrated Network for Image Light Source Transfer](https://mlsanity.com/paper/2104.08838/) (2021)
- [SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition](https://mlsanity.com/paper/2110.12914/) (2021)
- [Controllable Data Augmentation Through Deep Relighting](https://mlsanity.com/paper/2110.13996/) (2021)
- [Computational Flash Photography through Intrinsics](https://mlsanity.com/paper/2306.06089/) (2023)
- [Physically Inspired Dense Fusion Networks for Relighting](https://mlsanity.com/paper/2105.02209/) (2021)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network](https://mlsanity.com/paper/2109.08174/) (2021)
- [SIRfyN: Single Image Relighting from your Neighbors](https://mlsanity.com/paper/2112.04497/) (2021)
- [Unsupervised network for low-light enhancement](https://mlsanity.com/paper/2306.02883/) (2023)
- [MLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields](https://mlsanity.com/paper/2411.17235/) (2024)
- [Unsupervised haze removal from underwater images](https://mlsanity.com/paper/2306.02912/) (2023)
- [Zero shot framework for satellite image restoration](https://mlsanity.com/paper/2306.02921/) (2023)
- [Learning Relighting and Intrinsic Decomposition in Neural Radiance Fields](https://mlsanity.com/paper/2406.11077/) (2024)
- [Geometric Similarity in VLM Low-Level Vision Representations](https://mlsanity.com/paper/2610.00848/) (2026)

## Abstract (arXiv)

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage.