META REALITY LABS RESEARCH
Photoreal Reconstruction
We present a method for photoreal scene reconstruction which uses RGB data from Aria glasses to recreate real scenes using a gaussian splatting representation.
HOW DOES IT WORK?
Reconstructing scenes from video captured on any egocentric device
This novel method aims to solve the limitations of creating high-quality scene reconstruction from wearable egocentric devices. We demonstrate a solution using visual-inertial bundle adjustment (VIBA) and incorporating a physical image formation model based on Gaussian Splatting.
APPLICATIONS
Reconstructing outdoor scenes at a large scale
Using this method, we create large-scale scene reconstructions from casual RGB captures from Aria Gen 1 glasses in diverse outdoor scenarios.
APPLICATIONS
Reconstructing high quality indoor scenes with lifted dynamic range
The illumination in indoor scenes can create challenges for the glasses form factor. By capturing videos with fast-exposures and visible noise, we create high dynamic range noise-free novel-view rendering.
APPLICATIONS
Improving reconstruction with online calibration
The human head moves fast and frequently. We use visual inertial bundle adjustment to online calibrate RGB cameras, and use that to further model a rolling-shutter aware Gaussian Splatting reconstruction.
APPLICATIONS
Creating high quality digital twin objects
We serve as a competitive baseline for high quality novel-view rendering for digital twin objects observed by Aria glasses.
Aria Scenes Dataset
Aria Scenes is a benchmark dataset for future research on photorealistic reconstruction. The dataset includes 12 .vrs files created in diverse indoor and outdoor environments.
EXPLORE THE DATASET
LEARN MORE ABOUT ARIA SCENES
Related Work
In addition to Photoreal Reconstruction, related works demonstrate high quality reconstruction results on egocentric data from Project Aria glasses.
Egocentric Sparse Reconstruction
Neural-PBIR
Learn more about Photoreal Reconstruction
For more information about our reconstruction method, check out our paper on arXiv.
READ THE PAPER
BibTex Citation
If you use the Aria Digital Twin Catalog in your research, please cite the following:
@inproceedings{lv2025egosplats,
title ={Photoreal Scene Reconstruction from an Egocentric Device},
author ={Lv, Zhaoyang and Monge, Maurizio and Chen, Ka and Zhu, Yufeng and Goesele, Michael and Engel, Jakob and Dong, Zhao and Newcombe, Richard},
booktitle ={ACM SIGGRAPH}
year ={2025}
}
Access Aria Scenes Dataset
If you are a researcher interested in recreating our method of scene reconstruction, access the Aria Scene Reconstruction Dataset to get started.
By submitting your email and accessing the Aria Scene Reconstruction Dataset, you agree to abide by the dataset license agreement and to receive emails in relation to Photoreal Reconstruction.
Subscribe to Project Aria Updates
Stay in the loop with the latest news from Project Aria.
By providing your email, you agree to receive marketing related electronic communications from Meta, including news, events, updates, and promotional emails related to Project Aria. You may withdraw your consent and unsubscribe from these at any time, for example, by clicking the unsubscribe link included on our emails. For more information about how Meta handles your data please read our Data Policy.
Follow Us
Sign up for our newsletter
By providing your email, you agree to receive marketing related electronic communications from Meta, including news, events, updates, and promotional emails related to Project Aria. You may withdraw your consent and unsubscribe from these at any time, for example, by clicking the unsubscribe link included on our emails.