A Gaze-depth Estimation Technique with an Implicit and Continuous Data Acquisition for OST-HMDs

The algorithm proposed in this paper is also an implicit SOM surface recon- struction, however, in a major difference with [YIL08] and all the other previously proposed SOM methods,

We compared four different devices under a prede- fined test scenario - an Ultra Mobile PC (UMPC), a Tablet PC, a monocular Head-Mounted see-through Display and a binocular

In this paper, we evaluate three confirmation methods, Hand-held (pressing a hand-held button), Voice (verbally re- porting) and Waiting (detecting nearly no movement of the head for

In this paper, we utilize this concept for accurate head tracking by fitting a statistical 3D model to marker-less face data acquired with a low-cost depth sensor, and demonstrate

Further, we present a concept of a low-cost head-mounted display with eye tracking and discuss applications which strongly depend on or benefit from gaze estimation.. Categories

Therefore, this section provides a quantitative evaluation of the accuracy of depth image based 3D difference detection with different setups (image based camera pose estimation with

In order to support their workflow and provide the user with an intuitive navigation tool for the high-dimensional multi-modal data, we propose a schematic view of the depth

Our method assumes that a model of the en- vironment relative to the eye camera is available and uses a hybrid approach, an adaptation of active gaze estimation methods for