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Augmented Reality (AR) teleconferencing allows spatially distributed users to interact with each other in 3D through agents in their own physical environments. Existing methods leveraging volumetric capturing and reconstruction can provide a high-fidelity experience but are often too complex and expensive for everyday use. Other solutions target mobile and effortless-to-setup teleconferencing on AR Head Mounted Displays (HMD). They directly transplant the conventional video conferencing onto an AR-HMD platform or use avatars to represent remote participants. However, they can only support either a high fidelity or a high level of co-presence. Moreover, the limited Field of View (FoV) of HMDs could further degrade users' immersive experience. To achieve a balance between fidelity and co-presence, we explore using life-size 2D video-based avatars (video avatars for short) in AR teleconferencing. Specifically, with the potential effect of FoV on users' perception of proximity, we first conducted a pilot study to explore the local-user-centered optimal placement of video avatars in small-group AR conversations. With the placement results, we then implement a proof-of-concept prototype of video-avatar-based teleconferencing. We conduct user evaluations with our prototype to verify its effectiveness in balancing fidelity and co-presence. Following the indication in the pilot study, we further quantitatively explore the effect of FoV size on the video avatar's optimal placement through a user study involving more FoV conditions in a VR-simulated environment. We regress placement models to serve as references for computationally determining video avatar placements in such teleconferencing applications on various existing AR HMDs and future ones with bigger FoVs.

Avatar-mediated symmetric Augmented Reality (AR) telepresence has emerged with the ability to empower users located in different remote spaces to interact with each other in 3D through avatars. However, different spaces have heterogeneous structures and features, which bring difficulties in synchronizing avatar motions with real user motions and adapting avatar motions to local scenes. To overcome these issues, existing methods generate mutual movable spaces or retarget the placement of avatars. However, these methods limit the telepresence experience in a small sub-area space, fix the positions of users and avatars, or adjust the beginning/ending positions of avatars without presenting smooth transitions. Moreover, the delay between the avatar retargeting and users' real transitions can break the semantic synchronization between users' verbal conversation and perceived avatar motion. In this paper, we first examine the impact of the aforementioned transition delay and explore the preferred transition style with the existence of such delay through user studies. With the results showing a significant negative effect of avatar transition delay and providing the design choice of the transition style, we propose a Predict-and-Drive controller to diminish the delay and present the smooth transition of the telepresence avatar. We also introduce a grouping component as an upgrade to immediately calculate a coarse virtual target once the user initiates a transition, which could further eliminate the avatar transition delay. Once having the coarse virtual target or an exactly predicted target, we find the corresponding target for the avatar according to the pre-constructed mapping of objects of interest between two spaces. The avatar control component maintains an artificial potential field of the space and drives the avatar towards the target while respecting the obstacles in the physical environment. We further conduct ablation studies to evaluate the effectiveness of our proposed components.

We present a novel method that robustly estimates the reflectance, even in an environment with dynamically changing light. To control the appearance of an object by using a projector-camera system, an appropriate estimate of the object's reflectance is vital to the creation of an appropriate projection image. Most conventional estimation methods assume static light conditions; however, in practice, the appearance is affected by both the reflectance and environmental light. In an environment with dynamically changing light, conventional reflectance estimation methods require calibration every time the conditions change. In contrast, our method requires no additional calibration because it simultaneously estimates both the reflectance and environmental light. Our method is based on the concept of creating two different light conditions by switching the projection at a rate higher than that perceived by the human eye and captures the images of a target object separately under each condition. The reflectance and environmental light are then simultaneously estimated by using the pair of images acquired under these two conditions. We implemented a projector-camera system that switches the projection on and off at 120 Hz. Experiments confirm the robustness of our method when changing the environmental light. Further, our method can robustly estimate the reflectance under practical indoor lighting conditions.

Inspection tasks focus on observation of the environment and are required in many industrial domains. Inspectors usually execute these tasks by using a guide such as a paper manual, and directly observing the environment. The effort required to match the information in a guide with the information in an environment and the constant gaze shifts required between the two can severely lower the work efficiency of inspector in performing his/her tasks. Augmented reality (AR) allows the information in a guide to be overlaid directly on an environment. This can decrease the amount of effort required for information matching, thus increasing work efficiency. AR guides on head-mounted displays (HMDs) have been shown to increase efficiency. Handheld AR (HAR) is not as efficient as HMD-AR in terms of manipulability, but is more practical and features better information input and sharing capabilities. In this study, we compared two handheld guides: an AR interface that shows 3D registered annotations, that is, annotations having a fixed 3D position in the AR environment, and a non-AR picture interface that displays non-registered annotations on static images. We focused on inspection tasks that involve high information density and require the user to move, as well as to perform several viewpoint alignments. The results of our comparative evaluation showed that use of the AR interface resulted in lower task completion times, fewer errors, fewer gaze shifts, and a lower subjective workload. We are the first to present findings of a comparative study of an HAR and a picture interface when used in tasks that require the user to move and execute viewpoint alignments, focusing only on direct observation. Our findings can be useful for AR practitioners and psychology researchers.

Virtual Reality (VR) Head-Mounted Displays (HMDs) are on the verge of becoming commodity hardware available to the average user and feasible to use as a tool for 3D work. Some HMDs include front-facing cameras, enabling Augmented Reality (AR) functionality. Apart from avoiding collisions with the environment, interaction with virtual objects may also be affected by seeing the real environment. However, whether these effects are positive or negative has not yet been studied extensively. For most tasks it is unknown whether AR has any advantage over VR. In this work we present the results of a user study in which we compared user performance measured in task completion time on a 9 degrees of freedom object selection and transformation task performed either in AR or VR, both with a 3D input device and a mouse. Our results show faster task completion time in AR over VR. When using a 3D input device, a purely VR environment increased task completion time by 22.5 percent on average compared to AR ( ). Surprisingly, a similar effect occurred when using a mouse: users were about 17.3 percent slower in VR than in AR ( ). Mouse and 3D input device produced similar task completion times in each condition (AR or VR) respectively. We further found no differences in reported comfort.

Visuo-haptic augmented reality systems enable users to see and touch digital information that is embedded in the real world. PHANToM haptic devices are often employed to provide haptic feedback. Precise co-location of computer-generated graphics and the haptic stylus is necessary to provide a realistic user experience. Previous work has focused on calibration procedures that compensate the non-linear position error caused by inaccuracies in the joint angle sensors. In this article we present a more complete procedure that additionally compensates for errors in the gimbal sensors and improves position calibration. The proposed procedure further includes software-based temporal alignment of sensor data and a method for the estimation of a reference for position calibration, resulting in increased robustness against haptic device initialization and external tracker noise. We designed our procedure to require minimal user input to maximize usability. We conducted an extensive evaluation with two different PHANToMs, two different optical trackers, and a mechanical tracker. Compared to state-of-the-art calibration procedures, our approach significantly improves the co-location of the haptic stylus. This results in higher fidelity visual and haptic augmentations, which are crucial for fine-motor tasks in areas such as medical training simulators, assembly planning tools, or rapid prototyping applications.

With the growing availability of optical see-through (OST) head-mounted displays (HMDs) there is a present need for robust, uncomplicated, and automatic calibration methods suited for non-expert users. This work presents the results of a user study which both objectively and subjectively examines registration accuracy produced by three OST HMD calibration methods: (1) SPAAM, (2) Degraded SPAAM, and (3) Recycled INDICA, a recently developed semi-automatic calibration method. Accuracy metrics used for evaluation include subject provided quality values and error between perceived and absolute registration coordinates. Our results show all three calibration methods produce very accurate registration in the horizontal direction but caused subjects to perceive the distance of virtual objects to be closer than intended. Surprisingly, the semi-automatic calibration method produced more accurate registration vertically and in perceived object distance overall. User assessed quality values were also the highest for Recycled INDICA, particularly when objects were shown at distance. The results of this study confirm that Recycled INDICA is capable of producing equal or superior on-screen registration compared to common OST HMD calibration methods. We also identify a potential hazard in using reprojection error as a quantitative analysis technique to predict registration accuracy. We conclude with discussing the further need for examining INDICA calibration in binocular HMD systems, and the present possibility for creation of a closed-loop continuous calibration method for OST Augmented Reality.