基于深度RNN模型的虚拟现实6DoF跟踪

Yun-Kai Chang, Mai-Keh Chen, Yun-Lun Li, Hao-Ting Li, Chen-Kuo Chiang
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引用次数: 2

摘要

本文提出了一种基于传感器信号的虚拟现实环境坐标跟踪方法。目的是将VR中的运动跟踪从3自由度(DOF)的旋转扩展到6自由度的位置加旋转。因此,我们可以在不使用VR设备提供的控制器或处理器的情况下跟踪VR坐标。提出了一种基于rnn的模型,在给定传感器信号的测量加速度和欧拉角的情况下,预测每个时间戳的位置位移。实验表明,该方法不仅能有效地预测传感器信号与位移之间的关系,还能有效地处理跟踪过程中的累积误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
6DoF Tracking in Virtual Reality by Deep RNN Model
In this paper, a novel coordinates tracking method is proposed for Virtual Reality (VR) environment using sensor signals. The purpose is to extend movement tracking in VR from 3 Degrees of Freedom (DOF) of rotation to 6DOF of position plus rotation. As a result, we can track VR coordinates without using controller or handler provided by VR devices. An RNN-based model is proposed to predict displacement of positions in each timestamp given measured acceleration and Euler angles from sensor signals. Experiments demonstrate that it is effective to predict correct position displacement, which not only models the relationship between sensor signals and displacement but also handles the cumulative errors during tracking.
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