By situating computer-generated content in the physical world, mobile augmented reality (AR) can support many tasks that involve effective search and inspection of physical environments. Currently, there is limited information regarding the viability of using AR in realistic wide-area outdoor environments and how AR experiences affect human behavior in these environments. Here, we conducted a wide-area outdoor AR user study ($n=48$) using a commercially available AR headset (Microsoft Hololens 2) to compare (1) user interactions with physical and virtual objects in the environment (2) the effects of different lighting conditions on user behavior and AR experience and (3) the impact of varying cognitive load on AR task performance. Participants engaged in a treasure hunt task where they searched for and classified virtual target items (green “gems”) in an augmented outdoor courtyard scene populated with physical and virtual objects. Cognitive load was manipulated so that in half the search trials users were required to monitor an audio stream and respond to specific target sounds. Walking paths, head orientation and eye gaze information were measured, and users were queried about their memory of encountered objects and provided feedback on the experience. Key findings included (1) Participants self-reported significantly lower comfort in the ambient natural light condition, with virtual objects more visible and participants more likely to walk into physical objects at night; (2) recall for physical objects was worse than for virtual objects, (3) participants discovered more gems hidden behind virtual objects than physical objects, implying higher attention on virtual objects and (4) dual-tasking modified search behavior. These results suggest there are important technical, perceptual and cognitive factors that must be considered if the full potential of “anywhere and anytime mobile AR” is to be realized.

Synthetic data is highly useful for training machine learning systems performing image-based 3D reconstruction, as synthetic data has applications in both extending existing generalizable datasets and being tailored to train neural networks for specific learning tasks of interest. In this paper, we introduce and utilize a synthetic data generation suite capable of generating data given existing 3D scene models as input. Specifically, we use our tool to generate image sequences for use with Multi-View Stereo (MVS), moving a camera through the virtual space according to user-chosen camera parameters. We evaluate how the given camera parameters and type of 3D environment affect how applicable the generated image sequences are to the MVS task using five pre-trained neural networks on image sequences generated from three different 3D scene datasets. We obtain generated predictions for each combination of parameter value and input image sequence, using standard error metrics to analyze the differences in depth predictions on image sequences across 3D datasets, parameters, and networks. Among other results, we find that camera height and vertical camera viewing angle are the parameters that cause the most variation in depth prediction errors on these image sequences.

Location-based or Out-of-Home Entertainment refers to experiences such as theme and amusement parks, laser tag and paintball arenas, roller and ice skating rinks, zoos and aquariums, or science centers and museums among many other family entertainment and cultural venues. More recently, location-based VR has emerged as a new category of out-of-home entertainment. These VR experiences can be likened to social entertainment options such as laser tag, where physical movement is an inherent part of the experience versus at-home VR experiences where physical movement often needs to be replaced by artificial locomotion techniques due to tracking space constraints. In this work, we present the first VR study to understand the impact of natural walking in a large physical space on presence and user preference. We compare it with teleportation in the same large space, since teleportation is the most commonly used locomotion technique for consumer, at-home VR. Our results show that walking was overwhelmingly preferred by the participants and teleportation leads to significantly higher self-reported simulator sickness. The data also shows a trend towards higher self-reported presence for natural walking.

Full-surround panoramic imagery can provide a viewer with a high-resolution visual impression of a pictured real or realistically rendered environment, but it does not provide as high a level of immersion as modeled 3D geometry can, when viewed with virtual reality (VR) headsets or projection-based setups. In this paper, we demonstrate that augmenting panorama images with geometrical models can be done simply in VR itself and can significantly increase the feeling of immersion a viewer experiences. We propose a novel interactive modeling tool that allows users to model geometry depicted in a surround-panoramic scene directly in VR, utilizing projection mapping of the panorama on top of the evolving geometry. The user interface is intuitive and allows novice users to produce geometry that approximates ground truth models sufficiently to enhance a user's VR viewing experience. We designed a user study that compares users' self-reported levels of immersion, scene realism, and discomfort on a set of created models and comparison cases. Our results indicate that our modeled scenes produce a significantly higher sense of immersion than a basic dome geometry for the panorama when viewed in VR with head orientation and position tracking.