As locomotion is an important factor in improving Virtual Reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenar-ios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.

Since for most consumers the Virtual Reality (VR) experience exceeds that of desktop applications, an increasing number of applications is being transferred from desktop to VR. Industrial and entertainment applications primarily expect for a richer consumer experience, while others, such as surgical applications, seek for improved precision over their desktop counterparts. One way to improve the performance of precision-based VR applications is to provide suitable visualizations. Today, these "suitable" visualizations are mostly transferred from desktop to VR without considering their spatial and temporal performance might change in VR. This may not lead to an optimal solution, which can be crucial for precision-based tasks. Misinterpretation of shape or distance in a surgical or pre-operative simulation can affect the chosen treatment and thus directly impact the outcome. Therefore, we evaluate the performance differences of multiple visualizations for 3D surfaces based on their shape and distance estimation for desktop and VR applications. We conducted a quantitative user study with 56 participants evaluating seven visualizations (Phong, Toon, Fresnel, Pseudo-Chromadepth, Heatmap, Isolines, and Arrow Glyphs). Our results show that the performance of each visualization varies depending on the task, system, and surface type, with VR generally providing improved results. While Isolines are able to improve distance estimation, Phong and Heatmaps are beneficial for shape estimation.