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Ego-OSCAR:开源自我中心立体采集系统

Ego-OSCAR: Egocentric Open source Stereo CAptuRe System

Gunjan Paul, Senthil Palanisamy, Satpal Singh Rathore, Pratyush Kumar Patnaik, Shubhanshu Khatana, Abhishek Anand

arXiv 2608.08285首次发表:更新:

发表机构

FPV Labs(FPV实验室)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本文提出低成本开源头戴式立体惯性采集设备Ego-OSCAR,配套软件栈与550小时带注释的自我中心立体视频数据集,旨在降低大规模自我中心数据采集的门槛,所有内容均已开源。

AI 中文摘要

我们提出Ego-OSCAR,这是一种用于野外自我中心数据采集的开源硬件、低成本头戴式立体惯性采集设备。EgoOSCAR将硬件同步的全局快门立体相机与6轴IMU、用于设备端视频编码的嵌入式Linux SBC,以及用于用户反馈和看门狗功能的实时微控制器配对。每台设备的完整物料清单成本低于200美元,仅使用商用组件和3D打印部件。除设备外,我们还发布了完整的软件栈(硬件加速录制管线、IMU采样守护进程、时间同步工具和看门狗固件),以及由分布式贡献者网络在日常室内环境中采集的、每台相机约550小时的自我中心立体视频,带有同步IMU数据。发布内容为带注释而非原始数据:开放式词汇的自由形式动作字幕覆盖了几乎整个记录时间线,每帧的3D手部重建与每会话的立体校准一同提供。Ego-OSCAR并不追求达到Project Aria等研究级系统的单设备保真度,其目标是成为众包自我中心采集的最便宜且合理的基础,并降低任何想要大规模采集自我中心数据的团队的启动门槛。所有硬件设计、软件和数据集均已开源。

英文摘要

We present Ego-OSCAR, an open-hardware, low-cost, head-mounted stereo-inertial capture device for egocentric data collection in the wild. EgoOSCAR pairs a hardware-synchronized global-shutter stereo camera with a 6- axis IMU, an embedded Linux SBC for on-device video encoding, and a realtime microcontroller for user feedback and watchdog functions. The complete bill of materials is under USD 200 per unit, using only commercially available components and 3D-printed parts. Alongside the device, we release a complete software stack (hardware-accelerated recording pipeline, IMU sampling daemon, time-synchronization tooling, and watchdog firmware) and roughly 550 hours of egocentric stereo video per camera with synchronized IMU, collected by a distributed contributor network across everyday indoor environments. The release is annotated rather than raw: free-form action captions cover essentially the entire recorded timeline with an open vocabulary, and per-frame 3D hand reconstructions ship alongside per-session stereo calibration. Ego-OSCAR does not aim to match the per-unit fidelity of research-grade systems such as Project Aria; it aims to be the cheapest defensible substrate for crowdsourced egocentric capture, and to lower the activation energy for any team that wants to collect egocentric data at scale. All hardware designs, software, and the dataset are open-sourced

论文原文

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