We performed a mixed-design study with 56 participants to compare the effect of horizontal FOV (hfov) and vertical FOV (vfov) on egocentric distance perception in four different realistic virtual environments (VEs). We also compared VE attributes of indoor/outdoor and cluttered/uncluttered. The participants blind-walked towards four different targets at 3m, 4m, 5m, and 6m distance while wearing a backpack computer and a wide FOV head-mounted display (HMD). The combinations of 165°, 110° and 45° hfovs, and 110° and 35° vfovs was simulated in the same HMD. The results indicated more accurate distance judgement with larger hfov with no significant effect of vfov. More accurate distance judgement in indoor VEs compared to outdoor VEs was observed. Also, participants judged distances more accurately in cluttered environments versus uncluttered environments. These results highlight that the environment is important in distance-critical VR applications and wider hfov should be considered for an improved distance judgment.

In recent years, pass-through cameras have resurfaced as inclusions for virtual reality (VR) hardware. With modern cameras that now have increased resolution and frame rate, Video See-Through (VST) Head-Mounted Displays (HMD) can be used to provide an Augmented Reality (AR) experience. However, because users see their surroundings through video capture and HMD lenses, there is question surrounding how people perceive their environment with these devices. We conducted a user study with 26 participants to help understand if distance perception is altered when viewing surroundings with a VST HMD. Although previous work shows that distance estimation in VR with an HTC Vive is comparable to that in the real world, our results show that the inclusion of a ZED Mini pass-through camera causes a significant difference between normal, unrestricted viewing and that through a VST HMD.

In this paper we present AffordIt!, a tool for adding affordances to the component parts of a virtual object. Following 3D scene reconstruction and segmentation procedures, users find themselves with complete virtual objects, but no intrinsic behaviors have been assigned, forcing them to use unfamiliar Desktop-based 3D editing tools. AffordIt! offers an intuitive solution that allows a user to select a region of interest for the mesh cutter tool, assign an intrinsic behavior and view an animation preview of their work. To evaluate the usability and workload of AffordIt! we ran an exploratory study to gather feedback. In the study we utilize two mesh cutter shapes that select a region of interest and two movement behaviors that a user then assigns to a common household object. The results show high usability with low workload ratings, demonstrating the feasibility of AffordIt! as a valuable 3D authoring tool. Based on these initial results we also present a road-map of future work that will improve the tool in future iterations.

Many companies within the Internet of Things (IoT) sector rely on the personal data of users to deliver and monetize their services, creating a high demand for personal information. A user can be seen as making a series of transactions, each involving the exchange of personal data for a service. In this paper, we argue that privacy can be described quantitatively, using the game- theoretic concept of value of information (VoI), enabling us to assess whether each exchange is an advantageous one for the user. We introduce PrivacyGate, an extension to the Android operating system built for the purpose of studying privacy of IoT transactions. An example study, and its initial results, are provided to illustrate its capabilities.