Vision-tangible mixed reality (VTMR) is a further development of the traditional mixed reality. It provides an experience of directly manipulating virtual objects at the perceptual level of vision. In this paper, we propose a mixed reality system called “VTouch”. VTouch is composed of an optical see-through head-mounted display (OST-HMD) and a depth camera, supporting a direct 6 degree-of-freedom transformation and a detailed manipulation of 6 sides of the Rubik’s cube. All operations can be performed based on the spatial physical detection between virtual and real objects. We have not only implemented a qualitative analysis of the effectiveness of the system by a functional test, but also performed quantitative experiments to test the effects of depth occlusion. In this way, we put forward basic design principles and give suggestions for future development of similar systems. This kind of mixed reality system is significant for promoting the development of the intelligent environment with state-of-the-art interaction techniques.
Single point active alignment method is a widely used calibration method for optical-see-through head-mounted displays (OST-HMDs) since its appearance. It always requires high-accuracy alignment for data acquisition, and the collected data affect the calibration accuracy to a large extent. However, there are often many kinds of alignment errors occurring in the calibration process. These errors may contain random errors of manual alignment and system errors of the fixed eye-HMD model. To tackle these problems, we first leverage a random sample consensus approach to recurrently decrease the random error of the collected data sequence and use a region-induced data enhancement method to reduce the system error. We design a typical framework to enhance the data acquisition for calibration, sequentially reducing the random error and the system error. Experimental results show that the proposed method can significantly make the calibration more robust due to the elimination of sampling points with large errors. At the same time, the calibration accuracy can be increased by the proposed dynamic eye-HMD model that takes the eye movement into consideration. The improvement about calibration should be significant to promote the applications based on OST-HMDs.
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