MonoEgo：利用被动腕部星座与稀疏工作站锚点的单目度量第一人称演示捕捉

中文摘要 AI 辅助

图像对齐的度量演示通常需要专用的跟踪硬件和跨设备同步。我们提出MonoEgo，一种捕捉系统，用离线单目重建替代主动腕部仪器。一个90帧每秒的全局快门相机在共享图像时钟上观察校准的被动腕部星座、稀疏工作站锚点和场景。MonoTag SLAM将标记角点与ORB几何结合，并利用视觉证据拒绝模糊的平面标记姿态。其度量Atlas支持区间尺度重新锚定、验证地图合并，以及由最终地图支持的早期帧的回顾性定位。相机和腕部星座输出保持有效性和地图来源，不支持的运动被留作缺失。实验显示，在连续锚点可见性之外进行度量跟踪，支持地图组件的重新连接，以及恢复一些缺失的相机姿态。与多传感器相机参考和独立静止星座测试的比较，表征了轨迹一致性和精度，同时揭示了不完整的覆盖和残余几何不确定性。结果表明，被动固定装置和离线重建可以减少捕捉侧需求。动态精度、部署和下游策略益处需进一步研究。

英文摘要

Image-aligned metric demonstrations often require dedicated tracking hardware and synchronization across devices. We present MonoEgo, a capture system that replaces active wrist instrumentation with offline monocular reconstruction. One 90-FPS global-shutter camera observes calibrated passive wrist constellations, sparse workstation anchors, and the scene on a shared image clock. MonoTag SLAM combines marker corners with ORB geometry and uses visual evidence to reject ambiguous planar-marker poses. Its metric Atlas supports interval scale re-anchoring, verified map merging, and retrospective localization of earlier frames supported by the final map. Camera and wrist-constellation outputs retain validity and map provenance, and unsupported motion is left missing. Experiments show metric tracking beyond continuous anchor visibility, reconnection of supported map components, and recovery of some missing camera poses. Comparisons against a multisensor camera reference and separate stationary-constellation tests characterize trajectory agreement and precision while revealing incomplete coverage and residual geometric uncertainty. The results indicate that passive fixtures and offline reconstruction can reduce capture-side requirements. Dynamic accuracy, deployment, and downstream policy benefits require further study.

发表机构

• Anyverse Dynamics

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