Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
Abstract
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth. This ADT release contains 200 sequences of real-world activities conducted by Aria wearers in two real indoor scenes with 398 object instances (324 stationary and 74 dynamic). Each sequence consists of: a) raw data of two monochrome camera streams, one RGB camera stream, two IMU streams; b) complete sensor calibration; c) ground truth data including continuous 6-degree-of-freedom (6DoF) poses of the Aria devices, object 6DoF poses, 3D eye gaze vectors, 3D human poses, 2D image segmentations, image depth maps; and d) photo-realistic synthetic renderings. To the best of our knowledge, there is no existing egocentric dataset with a level of accuracy, photo-realism and comprehensiveness comparable to ADT. By contributing ADT to the research community, our mission is to set a new standard for evaluation in the egocentric machine perception domain, which includes very challenging research problems such as 3D object detection and tracking, scene reconstruction and understanding, sim-to-real learning, human pose prediction - while also inspiring new machine perception tasks for augmented reality (AR) applications. To kick start exploration of the ADT research use cases, we evaluated several existing state-of-the-art methods for object detection, segmentation and image translation tasks that demonstrate the usefulness of ADT as a benchmarking dataset.
Cite
@article{arxiv.2306.06362,
title = {Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception},
author = {Xiaqing Pan and Nicholas Charron and Yongqian Yang and Scott Peters and Thomas Whelan and Chen Kong and Omkar Parkhi and Richard Newcombe and Carl Yuheng Ren},
journal= {arXiv preprint arXiv:2306.06362},
year = {2023}
}