Our paper entitled ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapes has been accepted for publication and oral presentation to IEEE VR 2020.
Abstract: We present ReViVD, a tool for exploring and filtering large trajectory-based datasets using virtual reality. ReViVD's novelty lies in using simple 3D shapes—such as cuboids, spheres and cylinders—as queries for users to select and filter groups of trajectories. Building on this simple paradigm, more complex queries can be created by combining previously made selection groups through a system of user-created Boolean operations. We demonstrate the use of ReViVD in different application domains, from GPS position tracking to simulated data (e. g., turbulent particle flows and traffic simulation). Our results show the ease of use and expressiveness of the 3D geometric shapes in a broad range of exploratory tasks. ReViVD was found to be particularly useful for progressively refining selections to isolate outlying behaviors. It also acts as a powerful communication tool for conveying the structure of normally abstract datasets to an audience.