A Clustering Approach to Unveil User Similarities in 6 df Extended Reality Applications

Abstract

The advent in our daily life of Extended Reality (XR) technologies, such as Virtual and Augmented Reality, has led to the rise of user-centric systems, offering higher level of interaction and presence in virtual environments. In this context, understanding the actual interactivity of users is still an open challenge and a key step to enabling user-centric system. In this work, our goal is to construct an efficient clustering tool for 6 df navigation trajectories by extending the applicability of existing behavioural tool. Specifically, we first compare the navigation in 6 df with its 3 df counterpart, highlighting the main differences and novelties. Then, we investigate new metrics aimed at better modelling behavioural similarities between users in a 6 df system. More concretely, we define and compare 11 similarity metrics which are based on different distance features (i.e., user positions in the 3D space, user viewing directions) and distance measurements (i.e., Euclidean, Geodesic, angular distance). Our solutions are validated and tested on real navigation paths of users interacting with dynamic volumetric media in both 6 df Virtual Reality and Augmented Reality conditions. Results show that metrics based on both user position and viewing direction better perform in detecting user similarity while navigating in a 6 df system. Such easy-to-use but robust metrics allow us to answer a fundamental question for user-centric systems: 'How do we detect if users look at the same content in 6 df?', opening the gate to new solutions based on users interactivity, such as viewport prediction, live streaming services optimised based on users behaviour but also for user-based quality assessment methods.