We present a novel selection technique for VR called LenSelect. The main idea is to decrease the Index of Difficulty (ID) according to Fitts’ Law by dynamically increasing the size of the potentially selectable objects. This facilitates the selection process especially in cases of small, distant or partly occluded objects, but also for moving targets. In order to evaluate our method, we have defined a set of test scenarios that covers a broad range of use cases, in contrast to often used simpler scenes. Our test scenarios include practically relevant scenarios with realistic objects but also synthetic scenes, all of which are available for download. We have evaluated our method in a user study and compared the results to two state-of-the-art selection techniques and the standard ray-based selection. Our results show that LenSelect performs similar to the fastest method, which is ray-based selection, while significantly reducing the error rate by 44%.
Locomotion in virtual reality (VR) remains challenging due to limitations of common input methods. Sedentary input devices may endanger immersion, real-tovirtual world perception dissonance can lead to simulator sickness, and physical input devices such as framed walking dishes are often complex and expensive. We present a low-cost, easy to use, easy to manufacture, and easily portable device for locomotion in VR based on a hoverboard metaphor. Building on related work and our own iterative VR locomotion system designs we hypothesize that hoverboarding can provide a compelling and intuitive method for short-and long-distance locomotion in VR with a potential to reduce simulator sickness due to consistent and stable locomotion that corresponds well to the physical proprioception of the users while navigating VR. We discuss design iterations of our device prototypes, promising results from an early explorative evaluation, as well as ongoing continued work.
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