On temporal-spatial realism in the virtual reality environment

Abstract

The Polhemus Isotrak is often used as an orientation and position tracking device in virtual reality e n vironments. When it is used to dynamically determine the user's viewpoint and line of sight e.g. in the case of a head mounted display the noise and delay in its measurement data causes temporal-spatial distortion, perceived by the user as jittering of images and lag between head movement and visual feedback. To tackle this problem, we rst examined the major cause of the distortion, and found that the lag felt by the user is mainly due to the delay in orientation data, and the jittering of images is caused mostly by the noise in position data. Based on these observations, a predictive Kalman lter was designed to compensate for the delay in orientation data, and an anisotropic low pass lter was devised to reduce the noise in position data. The e ectiveness and limitations of both approaches were then studied, and the results shown to be satisfactory.

Realizing a low-latency virtual reality environment for motor learning

“…In order to evaluate and compare the overall end-to-end latency of different rendering and tracking approaches, we adopted and extended a well-established latency measurement approach [Liang et al 1991;Steed 2008;Friston and Steed 2014]: Typically, a pendulum is placed inside the tracking area, and the tracking data is visualized on a display behind the pendulum. A high speed camera records both the swinging real pendulum and the virtual pendulum on the screen.…”

Effect of latency on presence in stressful virtual environments

“…[Ware 1994], [Liang 1991], and [Miné 1993] describe techniques for making these measurements. Latency is known to have an adverse effect on both user performance and comfort in VEs.…”

On error bound estimation for motion prediction

“…Results show that during rapid motion, the predictor becomes less effective and its performance is similar to one without using the Kalman filter. Another method is to use the Gauss-Markov process model to predict head motion [14] and human motion [3]. In [18], a grey system theory-based predictor, which accuracy is similar to the polynomial-based predictor with Kalman filtering, is used to predict head motion.…”