Summary of Aria Everyday Activities Dataset, by Zhaoyang Lv et al.

Aria Everyday Activities Dataset

by Zhaoyang Lv, Nicholas Charron, Pierre Moulon, Alexander Gamino, Cheng Peng, Chris Sweeney, Edward Miller, Huixuan Tang, Jeff Meissner, Jing Dong, Kiran Somasundaram, Luis Pesqueira, Mark Schwesinger, Omkar Parkhi, Qiao Gu, Renzo De Nardi, Shangyi Cheng, Steve Saarinen, Vijay Baiyya, Yuyang Zou, Richard Newcombe, Jakob Julian Engel, Xiaqing Pan, Carl Ren

First submitted to arxiv on: 20 Feb 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The researchers present the Aria Everyday Activities (AEA) Dataset, a multimodal open dataset recorded using Project Aria glasses. The dataset contains 143 daily activity sequences from five indoor locations, including various sensor data and machine perception data such as high-frequency 3D trajectories and speech transcriptions. This paper demonstrates several research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This dataset is a collection of daily activities recorded using special glasses. It has lots of information like where people are looking, what they’re saying, and how they’re moving. The researchers show some cool things that can be done with this data, like creating maps of the rooms and understanding what people are talking about.