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Hand Waving Away Scale

机译:挥手秤

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摘要

This paper presents a novel solution to the metric reconstruction of objects using any smart device equipped with a camera and an inertial measurement unit (IMU). We propose a batch, vision centric approach which only uses the IMU to estimate the metric scale of a scene reconstructed by any algorithm with Structure from Motion like (SfM) output. IMUs have a rich history of being combined with monocular vision for robotic navigation and odometry applications. These IMUs require sophisticated and quite expensive hardware rigs to perform well. IMUs in smart devices, however, are chosen for enhancing interactivity - a task which is more forgiving to noise in the measurements. We anticipate, however, that the ubiquity of these "noisy" IMUs makes them increasingly useful in modem computer vision algorithms. Indeed, we show in this work how an IMU from a smart device can help a face tracker to measure pupil distance, and an SfM algorithm to measure the metric size of objects. We also identify motions that produce better results, and develop a heuristic for estimating, in real-time, when enough data has been collected for an accurate scale estimation.
机译:本文提出了一种新颖的解决方案,可使用任何配备了摄像头和惯性测量单元(IMU)的智能设备对物体进行度量重建。我们提出了一种以视觉为中心的批处理方法,该方法仅使用IMU来估计由具有类似运动的结构(SfM)输出的任何算法重建的场景的度量尺度。 IMU具有与单目视觉相结合的丰富历史,可用于机器人导航和里程表应用。这些IMU需要复杂且昂贵的硬件设备才能发挥良好的性能。但是,选择智能设备中的IMU来增强交互性-这项任务更加宽容了测量中的噪声。但是,我们预计,这些“嘈杂的” IMU的普遍存在使它们在现代计算机视觉算法中变得越来越有用。确实,我们在这项工作中展示了来自智能设备的IMU如何帮助面部跟踪器测量瞳孔距离,以及SfM算法如何测量对象的度量大小。当收集到足够的数据以进行准确的比例估算时,我们还将识别产生更好结果的运动,并开发一种启发式算法,以进行实时估算。

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