# Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality

arXiv: https://arxiv.org/abs/2301.06143 · HTML: https://mlsanity.com/paper/2301.06143/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Fast Spatially-Varying Indoor Lighting Estimation](https://mlsanity.com/paper/1906.03799/) (2019)
- [HDR Environment Map Estimation for Real-Time Augmented Reality](https://mlsanity.com/paper/2011.10687/) (2020)
- [Attention Mesh: High-fidelity Face Mesh Prediction in Real-time](https://mlsanity.com/paper/2006.10962/) (2020)
- [Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels](https://mlsanity.com/paper/2003.14299/) (2020)
- [PointAR: Efficient Lighting Estimation for Mobile Augmented Reality](https://mlsanity.com/paper/2004.00006/) (2020)
- [Face Deblurring using Dual Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2207.11617/) (2022)
- [Learning Illumination from Diverse Portraits](https://mlsanity.com/paper/2008.02396/) (2020)
- [Lighting, Reflectance and Geometry Estimation from 360circ Panoramic Stereo](https://mlsanity.com/paper/2104.09886/) (2021)
- [Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality](https://mlsanity.com/paper/2106.15280/) (2021)
- [ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones](https://mlsanity.com/paper/2108.10515/) (2021)

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

- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality](https://mlsanity.com/paper/2411.02179/) (2024)
- [IPCD: Intrinsic Point-Cloud Decomposition](https://mlsanity.com/paper/2511.09866/) (2025)

## Abstract (arXiv)

Lighting understanding plays an important role in virtual object composition, including mobile augmented reality (AR) applications. Prior work often targets recovering lighting from the physical environment to support photorealistic AR rendering. Because the common workflow is to use a back-facing camera to capture the physical world for overlaying virtual objects, we refer to this usage pattern as back-facing AR. However, existing methods often fall short in supporting emerging front-facing mobile AR applications, e.g., virtual try-on where a user leverages a front-facing camera to explore the effect of various products (e.g., glasses or hats) of different styles. This lack of support can be attributed to the unique challenges of obtaining 360^circ HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques. In this paper, we propose to leverage dual-camera streaming to generate a high-quality environment map by combining multi-view lighting reconstruction and parametric directional lighting estimation. Our preliminary results show improved rendering quality using a dual-camera setup for front-facing AR compared to a commercial solution.