Less Is More: Efficient Networked VR Transformation Handling Using Geometric Algebra

TL;DR

This paper introduces two algorithms using geometric algebra for efficient transmission and recording of VR transformation data in networked multiplayer environments, reducing bandwidth and improving visual quality.

Contribution

The paper presents novel algorithms based on dual-quaternions and multivectors for efficient VR data transmission and a new method for effective VR session recording.

Findings

Reduced network data transmission using the proposed algorithms.

Achieved better visual interpolation results compared to state-of-the-art methods.

Recorded data sets are quantitatively close to original data with less storage.

Abstract

As shared, collaborative, networked, virtual environments become increasingly popular, various challenges arise regarding the efficient transmission of model and scene transformation data over the network. As user immersion and real-time interactions heavily depend on VR stream synchronization, transmitting the entire data sat does not seem a suitable approach, especially for sessions involving a large number of users. Session recording is another momentum-gaining feature of VR applications that also faces the same challenge. The selection of a suitable data format can reduce the occupied volume, while it may also allow effective replication of the VR session and optimized post-processing for analytics and deep-learning algorithms. In this work, we propose two algorithms that can be applied in the context of a networked multiplayer VR session, to efficiently transmit the displacement and orientation data from the users' hand-based VR HMDs. Moreover, we present a novel method describing effective VR recording of the data exchanged in such a session. Our algorithms, based on the use of dual-quaternions and multivectors, impact the network consumption rate and are highly effective in scenarios involving multiple users. By sending less data over the network and interpolating the in-between frames locally, we manage to obtain better visual results than current state-of-the-art methods. Lastly, we prove that, for recording purposes, storing less data and interpolating them on-demand yields a data set quantitatively close to the original one.