ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapes

TL;DR

ReViVD is a virtual reality tool that uses simple 3D shapes for exploring and filtering large trajectory datasets, enabling intuitive selection, complex querying, and effective visualization across various domains.

Contribution

It introduces a novel VR-based approach utilizing basic 3D shapes and Boolean operations for trajectory data exploration and filtering, enhancing usability and expressiveness.

Findings

Ease of use demonstrated across multiple datasets

Effective in isolating outliers and complex behaviors

Facilitates communication of abstract data structures

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.