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Maps have long been a favored tool for navigation in both physical and virtual environments. As a navigation aid in virtual reality, map content and appearance can differ significantly. In this paper, three mini-maps are addressed: the WiM-3DMap, which provides a standard World-in-Miniature of the city model; the novel UC-3DMap, featuring important landmarks alongside ordinary buildings within the user’s vicinity; and the LM-3DMap, presenting only important landmarks. These mini-maps offer varying levels of building detail, potentially affecting spatial knowledge acquisition performance in diverse ways. A comparative study was conducted to evaluate the effectiveness of WiM-3DMap, UC-3DMap, LM-3DMap, and a baseline condition without a mini-map in spatial tasks such as spatial updating, landmark recall, landmark placement, and route recall. The findings demonstrated that LM-3DMap and UC-3DMap outperform WiM-3DMap in the tasks of spatial updating, landmark placement and route recall. However, the absence of detailed local context around the user may impede the effectiveness of LM-3DMap, as evidenced by UC-3DMap’s superior performance in the landmark placement task. These findings underscore the differences in effectiveness among various mini-maps that present distinct levels of building detail. A key conclusion is that including ordinary building information in the user’s immediate surroundings can significantly enhance the performance of a mini-map relying solely on landmarks. 

This empirical evaluation aimed to investigate how size perception differs between OST AR and the real world, focusing on two judgment methods: verbal reports and physical judgments. Using a within-subjects experimental design, participants viewed target objects in different sizes in both AR and real-world conditions and estimated their sizes using verbal and physical judgment methods across multiple trials. The study addressed two key hypotheses: (H1) that size perception in AR would differ from the Real World, potentially due to rendering limitations in OST-HMDs, and (H2) that verbal reports and physical judgments would yield different levels of accuracy due to distinct cognitive and perceptual processes involved in each method. Our findings supported these hypotheses, revealing key differences in size perception between the two judgment methods and viewing conditions. Participants consistently underestimated object sizes when using verbal reports in both AR and real-world conditions, with more pronounced errors in AR. In contrast, physical judgments yielded more accurate size estimates under both viewing conditions. Notably, the accuracy of verbal reports decreased as target sizes increased, a trend that was particularly evident in AR. These results underscore the perceptual challenges associated with verbal size judgments in AR and their potential limitations in applications requiring precise size estimations. By highlighting the differences in accuracy and consistency between verbal and physical judgment methods, this study contributes to a deeper understanding of size perception in OST AR and real-world contexts. 

Many AR applications require users to perceive, estimate and calibrate to the size of objects presented in the scene. Distortions in size perception in AR could potentially influence the effectiveness of skills transferred from the AR to the real world. We investigated the after-effects or carry-over effects of calibration of size perception in AR to the real world (RW), by providing feedback and an opportunity for participants to correct their judgments in AR. In an empirical evaluation, we employed a three-phase experiment design. In the pretest phase, participants made size estimations to target objects concurrently using both verbal reports and physical judgment in RW as a baseline. Then, they estimated the size of targets, and then were provided with feedback and subsequently corrected their judgments in a calibration phase. Followed by which, participants made size estimates to target objects in the real world. Our findings revealed that the carryover effects of calibration successfully transferred from AR to RW in both verbal reports and physical judgment methods. 

In this contribution, we propose to enhance two distant object manipulation techniques, BMSR (Bimanual Near-Field Metaphor with Scaled Replica) and the classic Scaled HOMER (Scaled Hand-Centered Object Manipulation Extending Ray Casting), via nearfield scaled replica manipulation and viewing. In the proposed Direct BMSR, context replicas are displayed so that the target replica can be manipulated relative to its context, allowing the user to directly manipulate the target replica in their arm’s reach space. Some additional features were implemented to make Direct BMSR an effective interface for manipulating objects from a distance. We proposed Scaled HOMER+NFSRV, which augments Scaled HOMER with a near-field scaled replica view (NFSRV) of the target object and its context, enabling the user to observe how the target replica is manipulated in relation to its context in their arm’s reach space while manipulating it from a distance. We conducted a between-subjects empirical evaluation of BMSR, Direct BMSR, Scaled HOMER, and Scaled HOMER+NFSRV. Our findings revealed that Direct BMSR and Scaled HOMER+NFSRV significantly outperformed BMSR and Scaled HOMER, respectively, in terms of accuracy. This finding highlights the advantages of adding near-field scaled replica viewing and manipulation with respect to distant object manipulation. 

Redirected walking allows users to naturally locomote within virtual environments that are larger than or different in layout from the physically tracked space. In this paper, we proposed novel optimization-driven alignment-based and Artificial Potential Field (APF) redirected walking controllers, as well as an integrated version of the two. The first two controllers employ objective functions of one variable, which is the included angle between the user's heading vector and the target vector originating from the user's physical position. The optimized angle represents the physical cell that is best aligned with the virtual cell or the target vector on which the designated point has the minimum APF value. The derived optimized angle is used to finely set RDW gains. The two objective functions can be optimized simultaneously, leading to an integrated controller that is potentially able to take advantage of the alignment-based controller and APF-based controller. Through extensive simulation-based studies, we found that the proposed alignment-based and integrated controllers significantly outperform the state-of-the-art controllers and the proposed APF based controller in terms of the number of resets. Furthermore, the proposed alignment controller and integrated controller provide a more uniform likelihood distribution across distance between resets, as compared to the other controllers. 

The goal of this research is to provide much needed empirical data on how the fidelity of popular hand gesture tracked based pointing metaphors versus commodity controller based input affects the efficiency and speed-accuracy tradeoff in users’ spatial selection in personal space interactions in VR. We conduct two experiments in which participants select spherical targets arranged in a circle in personal space, or near-field within their maximum arms reach distance, in VR. Both experiments required participants to select the targets with either a VR controller or with their dominant hand’s index finger, which was tracked with one of two popular contemporary tracking methods. In the first experiment, the targets are arranged in a flat circle in accordance with the ISO 9241-9 Fitts’ law standard, and the simulation selected random combinations of 3 target amplitudes and 3 target widths. Targets were placed centered around the users’ eye level, and the arrangement was placed at either 60%, 75%, or 90% depth plane of the users’ maximum arm’s reach. In experiment 2, the targets varied in depth randomly from one depth plane to another within the same configuration of 13 targets within a trial set, which resembled button selection task in hierarchical menus in differing depth planes in the near-field. The study was conducted using the HTC Vive head-mounted display, and used either a VR controller (HTC Vive), low-fidelity virtual pointing (Leap Motion), or a high-fidelity virtual pointing (tracked VR glove) conditions. Our results revealed that low-fidelity pointing performed worse than both high-fidelity pointing and the VR controller. Overall, target selection performance was found to be worse in depth planes closer to the maximum arms reach, as compared to middle and nearer distances.