From Research to Practice: Survey and Taxonomy of Object Selection in Consumer VR Applications

Abstract

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.

Mykola Maslych

Computer Science PhD Candidate

My research interests include machine learning applied to 3D User interfaces and HCI in general.