Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Virtual Reality (VR) technology is increasingly employed in daylight and view-out research, yet many existing approaches rely on static imagery that fails to capture the dynamic, temporal qualities of real-world environments. To address this gap, we present a workflow that integrates real-time stereoscopic video into VR, enabling the representation of evolving daylight conditions and exterior views. This paper details step-by-step procedures for initial setup, video processing, experiment implementation, and the integration of physiological and eye-tracking data collection. By incorporating temporal dynamics and real-time data acquisition, our method addresses the limitations of static scene simulations. Leveraging real-time stereoscopic videos, this workflow enhances the realism and richness of captured environments for studying perceptual variables, such as environmental quality, occupant satisfaction, and user preferences. Key Innovations • Integration of real-time stereoscopic video to represent temporal dynamics in VR • Detailed methodology for capturing and processing immersive daylight and view-out scenarios • Seamless incorporation of physiological and eyetracking data for comprehensive participant insights • Enhanced realism compared to static scene representations in built environment research Practical Implications Depending on the specific research questions or the type of scene, researchers may consider using dynamic stereoscopic VR scenes rather than static images. When feasible, systematically tracking participant responses and carefully integrating physiological and eye-tracking data offers more realistic, data-driven insights into occupant satisfaction and preference.