An underexplored interaction metaphor in virtual reality (VR) is throwing, with a considerable challenge in achieving accurate and natural results. We conducted an empirical investigation of participants’ performance in a VR throwing task, measuring their accuracy and preferences across Point of Release (PoR) mechanics (manual and automatic) with various input device categories (hand-held, on-body, external) and throwable object types. Participants were tasked with throwing a baseball, a bowling ball, and a football toward targets using 5 input configurations (2 manual and 3 automatic PoR). Results from 30 participants indicate that the overall highest accuracy was achieved with an automatic PoR configuration (on-body tracker). The post-study and VR survey results indicate that the majority of participants preferred a manual PoR configuration (hand-held VR controller-derived) for the throwing direction, throwing speed, and as being the closest to real-life throwing. Our findings are useful for VR researchers and developers who want to implement throwing as a technique in their applications.

Object selection has been explored extensively in the VR research literature. However, the research is typically conducted in constrained experimental setups. It remains unclear whether the designed selection techniques fit the prevalent practical uses and whether the experimental tasks represent important challenges in real applications. To identify and help bridge these gaps, we surveyed current consumer VR applications, containing 206 popular VR game and 3D modeling applications. We extracted 1300+ selection scenarios based on video analyses of these applications and derived a taxonomy to understand common patterns on where and how selections occur. Our findings reveal significant gaps in selection tasks and techniques between research and consumer applications. We also present an interactive visualization tool to help researchers explore the VR object selection scenarios. Finally, we discuss how our work can help researchers and developers evaluate techniques in meaningful tasks and drive the design of techniques.

In this paper, we compare the performance of a canonical genetic algorithm (CGA), the Self Adaptive Genetic Algorithm (SAGA), and a feed-forward neural network (FFNN) on a predictive modeling problem with incomplete data. Predictive modeling involves learning relationships between the features and labels of the data points in a dataset. Datasets with missing input values may cause problems for some learning algorithms by biasing the learned models. Imputation refers to techniques for replacing missing data through methods such as statistical probabilities, multivariate analysis, machine learning, or K-nearest neighbors.

Virtual reality (VR) researchers have had a long-standing interest in studying locomotion for developing new techniques, improving upon prior ones, and analyzing their effects on users. To help organize prior work, several researchers have presented taxonomies for categorizing locomotion techniques in general. More recently, researchers have begun to conduct systematic reviews to better understand what locomotion techniques have been investigated. In this paper, we present our own systematic review of locomotion techniques based on a well-established taxonomy, and we use k-means clustering to identify to what extent locomotion techniques have been explored. Our results indicate that selection-based, walking-based, and steering-based locomotion techniques have been moderately to highly explored while manipulation-based and automated locomotion techniques have been less explored. We also present results on what types of metrics have been used to evaluate locomotion techniques. While usability, discomfort, and travel performance metrics have been moderately to highly explored, other metrics, such as biometrics, user experience, and emotions, have been less explored.