EgoKit统一多设备头戴数据采集，支持六类异构设备同步录制。
EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices
- 统一六种设备的录制流程与日志格式，降低跨平台开发门槛。
- 支持头显设备记录头部姿态与26关节约束手部追踪数据。
- 配套腕部摄像头与转接头，无需定制硬件即可扩展视角。
第一人称视频在机器人学习、行为理解与具身AI研究中日益重要，但大规模采集仍面临碎片化问题：不同设备（如安卓手机、iPhone、iPad、智能眼镜、扩展现实头显）使用不同的SDK、相机访问策略及外部摄像头和本地追踪限制。因此，同步获取第一人称视角与手腕视角通常需绑定单一专有平台或定制专用装置。为解决该问题，本文提出EgoKit，一套可在六种异构主机设备上统一运行的第一人称数据采集工具包。所有支持设备均提供一致的录制交互与本地存储的标准化日志格式；在XR头显上，额外记录头部姿态与符合OpenXR标准的26关节手部追踪数据，并与视频流对齐。配套配件包括两个腕部摄像头、支架、头带和USB-C集线器，可无需定制硬件即为任意支持设备添加腕部视角。EgoKit已开源：https://egokit.chuange.org/。
原文摘要 · Abstract (English)
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/}.
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