eXtended reality (XR) technologies such as virtual reality and 360° stereoscopic streaming enable the concept of the Metaverse, an immersive virtual space for collaboration and interaction. To ensure a high-fidelity display of immersive media, the bandwidth, latency and network traffic patterns will need to be considered to ensure a user's Quality of Experience (QoE). In this article, examples and calculations are explored to demonstrate the requirements of the above-mentioned parameters. Additionally, future methods such as network-awareness using reinforcement learning (RL) and XR content awareness using spatial or temporal differences in the frames could be explored from a multimedia communications perspective.

In the last decade, the use of virtual reality (VR) technologies has been increasing, as head-mounted display devices, such as the Meta Quest series, have become commercially accessible to everyday consumers. There are plenty of software applications developed for VR, ranging from games to serious training. Despite the fast-growing emergence of novel and natural gesture-based interface components, traditional windows, icons, menus, and pointer user interfaces still remain popular in these implementations. Therefore, navigating and interacting within 3-dimensional environments can still be challenging in virtual reality. This body of work will serve as a reference to the literature survey on 3-dimensional user interfaces and their implementations, and categorize them to reiterate a set of design recommendations for implementing 3-dimensional user interfaces in virtual reality. While questions revolve around the need for a 3-dimensional user interface if a 2-dimensional user interface suffices, we cannot ignore the capabilities and benefits offered when utilising 3-dimensional space. Unlike other work, this work proposes a novel metaphor-guided quadrant model to delineate the 3-dimensional user interface problem in an end-to-end manner. To achieve this, over 30 unique articles selected using a meta-analyses methodology were reviewed. The result highlights the dimensional semantics of the inputs and dimensional outcomes of the virtual targets. Notable reiterated design recommendations would match devices to interaction and then to the tasks in 3-dimensional environments, reducing the degrees of freedom, imposing constraints when possible, and not implementing one single best interaction technique. The results of this survey will be beneficial for virtual reality simulation designers or researchers attempting to determine the appropriate model when developing user interfaces for virtual reality content.

Metaverse applications often require many new 3D point cloud models that are unlabeled and that have never been seen before; this limited information results in difficulties for data-driven model analyses. In this paper, we propose a novel data-driven 3D point cloud analysis network GN-CNN that is suitable for such scenarios. We tackle the difficulties with a few-shot learning (FSL) approach by proposing an unsupervised generative adversarial network GN-GAN to generate prior knowledge and perform warm start pre-training for GN-CNN. Furthermore, the 3D models in the Metaverse are mostly acquired with a focus on the models’ visual appearances instead of the exact positions. Thus, conceptually, we also propose to augment the information by unleashing and incorporating local variance information, which conveys the appearance of the model. This is realized by introducing a graph convolution-enhanced combined multilayer perceptron operation (CMLP), namely GCMLP, to capture the local geometric relationship as well as a local normal-aware GeoConv, namely GNConv. The GN-GAN adopts an encoder–decoder structure and the GCMLP is used as the core operation of the encoder. It can perform the reconstruction task. The GNConv is used as the convolution-like operation in GN-CNN. The classification performance of GN-CNN is evaluated on ModelNet10 with an overall accuracy of 95.9%. Its few-shot learning performance is evaluated on ModelNet40, when the training set size is reduced to 30%, the overall classification accuracy can reach 91.8%, which is 2.5% higher than Geo-CNN. Experiments show that the proposed method could improve the accuracy in 3D point cloud classification tasks and under few-shot learning scenarios, compared with existing methods such as PointNet, PointNet++, DGCNN, and Geo-CNN, making it a beneficial method for Metaverse applications.

This demo presents a virtual reality (VR) environment developed for evaluating signalized and unsignalized intersection scenarios using low cost head-mounted devices (HMD). Prior work focused on VR simulations that re-create less complex scenarios for the evaluation of different autonomous vehicle (AV) behavior and external human-machine interfaces (eHMIs). The proposed VR environment allows participants to walk across multiple road lanes, and experience and interact with high fidelity simulated traffic scenarios, including signalized and unsignalized intersections at 1:1 scale, with vehicular traffic making turns, and providing audio feedback. This simulation can be used for conducting user studies to test AV eHMIs or behavior interventions with participants.

Patient decontamination is the act of removing or neutralising hazardous substances from an affected individual. To ensure adequate emergency preparedness, regulations require hospitals to train personnel in decontamination procedures regularly. To supplement in-person training, a virtual reality (VR) system is being developed for the training of hospital staff members in the mass decontamination of hazardous and toxic materials (HAZMAT) and/or radioactively contaminated casualties. As a demonstration of the concept, a primary VR prototype is designed to help users familiarize themselves with a chemical scanner tool, intended for examining victims for residual chemical hazards. This initial prototype showcases the benefits of using VR to create training simulations, complementing existing decontamination training methods in a secure and cost-effective manner. The proposed approach features a modularized user-centric design, a novel scanning simulation, and a high-realism virtual environment and workflow to enhance training effectiveness. A pilot user study and assessment suggest that new users were able to achieve a significant level of competency with VR, compared to users who underwent traditional training.

The term vulnerable road users (VRU) refers to those at higher risk when in traffic. VRUs are usually unprotected while in motion and will sustain greater injuries in vehicular collisions, or potentially fatalities. In this group, pedestrians and cyclists therefore have a lower chance of survival in accidents when compared to other road users, and are likely to suffer sub-stantial implications from autonomous vehicle (AV) deployments. Hence, it is crucial to focus on their safety. First, a taxonomy of categories of common cyclist-vehicle traffic scenarios is defined for development and re-creation within a simulator. A virtual reality (VR) cycling simulator framework, populated with AVs, is then described. It is proposed for the VR simulator to be used to study the cyclists' behavior and actions towards simulations of a future traffic environment involving encountering AVs in various road scenarios.