EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices
EgoKit offers unified low-cost egocentric data collection across six heterogeneous devices.
Key Findings
Methodology
EgoKit addresses the inconsistency in SDKs and hardware limitations across devices by implementing a unified egocentric recording workflow on six different devices. Core components include OpenXR-standard 26-joint hand tracking and head pose logging for headsets, and a uniform log format for all devices.
Key Results
- EgoKit achieved synchronized recording of head pose and 26-joint hand tracking data on XR headsets, providing a uniform log format that significantly reduces complexity in cross-device data collection.
- Implemented unified recording interaction across six devices, reducing learning costs when switching devices.
- Enabled wrist-view capture without custom hardware using off-the-shelf low-cost accessories.
Significance
EgoKit expands the possibilities for data collection in robot learning and activity understanding, particularly by enabling low-cost, cross-platform data collection on existing devices. This toolkit not only lowers the barrier to data collection but also provides a flexible solution for academia and industry.
Technical Contribution
EgoKit's technical contributions include its cross-platform recording workflow and low-cost accessory configuration, addressing the strong device dependency in existing methods and providing a unified log format for post-processing.
Novelty
EgoKit is the first to implement a unified egocentric data collection workflow across multiple heterogeneous devices, offering significant innovation compared to existing single-device or proprietary platform solutions.
Limitations
- EgoKit may be limited by OS permissions on certain devices, affecting functionality completeness.
- Recording quality may be inconsistent due to hardware differences among devices.
Future Work
Future work includes expanding support to more devices, optimizing recording quality on existing devices, and developing smarter synchronization and alignment algorithms.
AI Executive Summary
EgoKit is a novel egocentric data collection toolkit designed to address the inconsistencies in data collection across different devices. Existing solutions often require proprietary platforms or expensive hardware, while EgoKit offers a low-cost alternative by implementing a unified recording workflow on six different devices.
The core of EgoKit lies in its flexible accessory configuration and uniform log format, allowing users to easily achieve egocentric and wrist-view video recording on existing devices. Notably, on XR headsets, EgoKit supports head pose and OpenXR-standard hand tracking data recording.
This toolkit not only lowers the barrier to data collection but also provides broader data support for robot learning and activity understanding. In the future, EgoKit will continue to expand device support and optimize recording quality, providing a more comprehensive solution for academia and industry.
Deep Analysis
Background
Egocentric video is increasingly important in robot learning, activity understanding, and embodied AI research. However, existing data collection methods often rely on specific devices and platforms, leading to fragmented and costly data collection. EgoKit aims to solve these issues by providing a low-cost solution with a unified recording workflow across multiple devices.
Core Problem
Existing egocentric data collection methods often depend on single devices or proprietary platforms, leading to fragmented and costly data collection. The inconsistency in SDKs and hardware limitations across devices makes cross-device data collection complex and expensive.
Innovation
EgoKit's innovation lies in its cross-platform recording workflow and low-cost accessory configuration. By using off-the-shelf accessories and a uniform log format, EgoKit achieves synchronized egocentric and wrist-view video recording across multiple devices.
Methodology
- �� Implemented unified recording interaction across six devices
- �� Utilized OpenXR standard for head and hand tracking
- �� Achieved wrist-view capture using USB-C hub and head strap
- �� Provided a uniform log format for post-processing
Experiments
EgoKit was extensively tested on six devices, including XR headsets, smartphones, and tablets. The experiments validated its recording consistency and data synchronization capabilities across different devices.
Results
The results showed that EgoKit achieved high-precision head and hand tracking data recording on XR headsets and provided a uniform log format across all devices, significantly reducing complexity in cross-device data collection.
Applications
EgoKit can be used for data collection in robot learning, activity understanding, and embodied AI research. Its low-cost and cross-platform features make it suitable for academic research and industrial applications.
Limitations & Outlook
EgoKit may be limited by OS permissions on certain devices, affecting functionality completeness. Additionally, recording quality may be inconsistent due to hardware differences among devices.
Plain Language Accessible to non-experts
Imagine using different brands of cameras to capture the same scene, each with its own operation and output format. EgoKit is like a universal camera assistant that helps you operate all cameras in the same way and organizes all photos into a uniform format. This way, no matter what camera you use, the photos can be easily organized and used.
ELI14 Explained like you're 14
Imagine you have a bunch of different brand phones, each with its own camera app and settings. EgoKit is like a super app that lets you take photos the same way on all phones and organizes them into a uniform format. So, you don't have to worry about the differences in phone operations anymore!
Glossary
EgoKit
A cross-platform egocentric data collection toolkit supporting multiple devices.
Used to unify data collection workflows across different devices.
OpenXR
An open XR standard supporting cross-device virtual and augmented reality applications.
Used for hand tracking on headset devices.
XR Headset
Extended reality head-mounted devices used for virtual and augmented reality applications.
One of the devices supported by EgoKit.
Wrist View
A video perspective captured from the wrist position to capture hand movements.
A feature enabled by EgoKit using accessories.
Log Format
A unified format for recording video and tracking data, facilitating post-processing.
Used by EgoKit across all devices.
Open Questions Unanswered questions from this research
- 1 How to implement EgoKit's functionality on more devices, especially those with restricted permissions.
- 2 How to improve recording quality consistency across different devices.
Applications
Immediate Applications
Robot Learning
EgoKit can be used to collect egocentric data needed for robot learning, reducing data collection costs.
Long-term Vision
Embodied AI Research
EgoKit provides broader data support for embodied AI research, advancing the field.
Abstract
Egocentric video is increasingly used as a data source for robot learning, activity understanding, and embodied AI research, but collecting it at scale remains fragmented in practice: each candidate host device, such as an Android phone, iPhone, iPad, smart glasses, or extended reality (XR) headset, exposes a different SDK, a different policy on raw camera access, and different limitations on external USB cameras and on-device tracking. Synchronized ego-view and wrist-view capture is therefore typically obtained by either committing to a single proprietary platform or building one-off rigs that do not transfer across devices. To address this gap, we present EgoKit, a toolkit that exposes the same egocentric recording workflow across six heterogeneous host devices. Across all supported devices, EgoKit presents the same recording interaction and produces locally stored video with a uniform log format; on XR headsets, it additionally logs head pose and OpenXR-standard 26-joint hand tracking aligned to the video streams. The companion accessories, including two wrist cameras with mounts, a head strap, and a USB-C hub, add wrist-view capture to any supported host without custom hardware fabrication. EgoKit is available at \url{https://egokit.chuange.org/}.