In this paper, we proposed a novel method for virtual reality (VR) sickness reduction based on dynamic field of view (FOV) processing. Dynamic FOV processing is performed based on the estimated VR sickness for each video frame. The level of sickness is estimated using VR sickness model, which is obtained by defining the relationship between the motion information and the measured VR sickness. For motion information analysis, subregion-based correspondence points tracking is used to efficiently remove outliers and prevent prediction error propagation. Amount of head dispersion is used as a quantitative VR sickness measure, which can be calculated from inertial measurement unit sensor in VR devices. The optimal FOV range was determined by experimentally validating a minimum FOV that can effectively reduce VR sickness with almost negligible loss in presence. The simulation results show a significant decrease of 37% compared to full FOV viewing, when FOV is dynamically varied between full and 60°.

在本文中，我们提出了一种基于动态视场（FOV）处理的减少虚拟现实（VR）疾病的新方法。基于每个视频帧的估计VR病态执行动态FOV处理。使用VR疾病模型估计疾病的程度，该模型是通过定义运动信息和测得的VR疾病之间的关系而获得的。对于运动信息分析，基于子区域的对应点跟踪用于有效去除异常值并防止预测误差传播。头部分散量用作定量的VR疾病度量，可以从VR设备中的惯性测量单位传感器计算得出。最佳FOV范围是通过实验验证可有效减少VR疾病且存在时几乎可忽略的损失的最小FOV来确定的。当FOV在全角和60°之间动态变化时，仿真结果显示与全FOV观看相比，显着降低了37％。