MOUNT: Learning 6DoF Motion Prediction Based on Uncertainty Estimation for Delayed AR Rendering

The delay of rendering on AR devices requires prediction of head motion using sensor data acquired tens of even one hundred milliseconds ago to avoid misalignment between the virtual content and the physical world, where the misalignment will lead to a sense of time latency and dizziness for users. To solve the problem, we propose a method for the 6DoF motion prediction to compensate for the time latency. Compared with traditional hand-crafted methods, our method is based on deep learning, which has better motion prediction ability to deal with complex human motion. In particular, we propose a MOtion UNcerTainty encode decode network (MOUNT) that estimates the uncertainty of input data and predicts the uncertainty of output motion to improve the prediction accuracy and smoothness. Experiments on the EuRoC and our collected dataset demonstrate that our method significantly outperforms the traditional method and greatly improves AR visual effects.