Referencing objects of interest is a common requirement in many collaborative tasks. Nonetheless, accurate object referencing at a distance can be challenging due to the reduced visibility of the objects or the collaborator and limited communication medium. Augmented Reality (AR) may help address the issues by providing virtual pointing rays to the target of common interest. However, such pointing ray techniques can face critical limitations in large outdoor spaces, especially when the environment model is unavailable. In this work, we evaluated two pointing ray techniques for distant object referencing in model-free AR from the literature: the Double Ray technique enhancing visual matching between rays and targets, and the Parallel Bars technique providing artificial orientation cues. Our experiment in outdoor AR involving participants as pointers and observers partially replicated results from a previous study that only evaluated observers in simulated AR. We found that while the effectiveness of the Double Ray technique is reduced with the additional workload for the pointer and human pointing errors, it is still beneficial for distant object referencing.

Augmented reality head-worn displays (AR HWDs) have the potential to assist personal computing and the acquisition of everyday information. In this research, we propose Glanceable AR, an interaction paradigm for accessing information in AR HWDs. In Glanceable AR, secondary information resides at the periphery of vision to stay unobtrusive and can be accessed by a quick glance whenever needed. We propose two novel hands-free interfaces: "head-glance", in which virtual contents are fixed to the user’s body and can be accessed by head rotation, and "gaze-summon" in which contents can be "summoned" into central vision by eye-tracked gazing at the periphery. We compared these techniques with a baseline heads-up display (HUD), which we call "eye-glance" interface in two dual-task scenarios. We found that the head-glance and eye-glance interfaces are more preferred and more efficient than the gaze-summon interface for discretionary information access. For a continuous monitoring task, the eye-glance interface was preferred. We discuss the implications of our findings for designing Glanceable AR interfaces in AR HWDs.

Specifying points in three-dimensional (3D) space is an essential function in many augmented reality (AR) applications. When an environment model is not available, a straightforward solution is to perform geometric triangulation using two rays. However, naïve implementations suffer from low accuracy caused by technical limitations of AR devices and human motor constraints. To overcome these issues, we designed and evaluated two enhanced geometric techniques for 3D point marking. VectorCloud uses multiple rays to reduce the effects of pointing jitter, and ImageRefinement improves the accuracy by allowing users to refine the 3D direction of the two rays. We conducted studies to understand the characteristics of these techniques in both ecologically valid outdoor settings using a mobile AR display and in more controlled setting using virtual reality simulation. Our experiments demonstrate that both techniques improve the precision of 3D point marking, and that ImageRefinement is superior to VectorCloud overall. These results are particularly relevant in the design of mobile AR systems intended for use in large outdoor areas.

This paper presents our solution to the 2019 3DUI Contest challenge, which focuses on passive haptics. We aimed to provide a compelling user experience in virtual reality while overcoming the limitations of physical space and current tracking devices. To meet these goals, we designed a time travel scenario that incorporates several novel features. A time machine increases efficiency in the use of physical space. A passive haptic camera prop provides a help system that is integrated into the storyline. Finally, the concept of a “temporal stabilizer” provides a plausible way to reuse a single tracking device to track multiple passive haptic props.

Specifying points in three-dimensional space is essential in AR applications. Geometric triangulation is a straightforward way to specify points, but its naïve implementation has low precision. We designed two enhanced geometric techniques for 3D point marking: VectorCloud, which uses multiple rays to reduce jittering, and ImageRefinement, which allows 3D ray refinement to improve precision. Our experiments, conducted in both simulated and real AR, demonstrate that both techniques improve the precision of 3D point marking, and that ImageRefinement is superior to VectorCloud overall. These results are particularly relevant in the design of mobile AR systems for large outdoor areas.

Specifying 3D positions in the real world is an essential step to create augmented reality content. However, this task can be challenging when information about the depth or geometry of the target location is not available. In this paper, we evaluate alternative techniques for 3D pointing at a distance without knowledge about the environment. We present the results of two studies evaluating the accuracy of techniques based on geometry and human depth perception. We find that geometric methods provide higher accuracy but may suffer from low precision due to pointing errors. We propose a solution that increases precision by combining multiple samples to obtain a better estimate of the target position.

In this paper, we present multiple 3D interaction techniques to address the three tasks in the 2018 3DUI contest: ladder climbing, drone flying and tower stacking. The proposed techniques provide smart interfaces to aid efficiency and error prevention. We discuss the principles and rationale used in the design.