Emerging XR applications, including Holography, Augmented, Virtual and Mixed Reality, are characterized by unprecedented requirements for Quality of experience (QoE), largely exceeding those currently attainable. To cope with these requirements, noticeable efforts and a number of initiatives are ongoing to enhance the current communications technologies, especially in the direction of supporting ultra-low latency and increased bandwidth. This work proposes an architecture that puts together the key enablers to support future XR applications, highlighting the shortcomings of existing technologies and leveraging the ongoing innovations. It demonstrates the feasibility of the proposed architecture by describing the processes driving the platform with relevant use case scenarios, and mapping the envisioned functionality to existing tools.
In recent years, the emergence of XR (eXtended Reality) applications, including Holography, Augmented, Virtual and Mixed Reality, has resulted in the creation of rather demanding requirements for Quality of Experience (QoE) and Quality of Service (QoS). In order to cope with requirements such as ultra-low latency and increased bandwidth, it is of paramount importance to leverage certain technological paradigms. The purpose of this paper is to identify these QoE and QoS requirements and then to provide an extensive survey on technologies that are able to facilitate the rather demanding requirements of Cloud-based XR Services. To that end, a wide range of enabling technologies are explored. These technologies include e.g. the ETSI (European Telecommunications Standards Institute) Multi-Access Edge Computing (MEC), Edge Storage, the ETSI Management and Orchestration (MANO), the ETSI Zero touch network & Service Management (ZSM), Deterministic Networking, the 3GPP (3rd Generation Partnership Project) Media Streaming, MPEG’s (Moving Picture Experts Group) Mixed and Augmented Reality standard, the Omnidirectional MediA Format (OMAF), ETSI’s Augmented Reality Framework etc.
The paper introduces the CHARITY framework, a novel framework which aspires to leverage the benefits of intelligent, network continuum autonomous orchestration of cloud, edge, and network resources, to create a symbiotic relationship between low and high latency infrastructures. These infrastructures will facilitate the needs of emerging applications such as holographic events, virtual reality training, and mixed reality entertainment. The framework relies on different enablers and technologies related to cloud and edge for offering a suitable environment in order to deliver the promise of ubiquitous computing to the NextGen application clients. The paper discusses the main pillars that support the CHARITY vision, and provide a description of the planned use cases that are planned to demonstrate CHARITY capabilities.
In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essentially a low-code metaverse authoring platform for developers to rapidly prototype high-fidelity and high-complexity medical simulations. MAGES breaks the authoring boundaries across extended reality, since networked participants can also collaborate using different virtual/augmented reality as well as mobile and desktop devices, in the same metaverse world. With MAGES we propose an upgrade to the outdated 150-year-old master-apprentice medical training model. Our platform incorporates, in a nutsell, the following novelties: a) 5G edge-cloud remote rendering and physics dissection layer, b) realistic real-time simulation of organic tissues as soft-bodies under 10ms, c) a highly realistic cutting and tearing algorithm, d) neural network assessment for user profiling and, e) a VR recorder to record and replay or debrief the training simulation from any perspective.THE MEDICAL METAVERSE, despite the inflated expectations, is steadily, albeit quietly, being created [15]. Along with it, many technical questions remain, including "who will build the medical metaverse and how?" Building such an ecosystem from few stakeholders would require Computer Graphics and Applications
We introduce a novel approach to reconstructing 3D objects from cross sections of point clouds acquired by 3D scanning. In this context cross sections are almost planar clusters of 3D points. We first thin each cluster to obtain an ordered one dimensional set of planar points. We then partition the point set to subsets that can be approximated adequately by piecewise quadratic rational Bezier curves using an optimal fitting method. For each curve we select a number of representative points that lie on the fitting curves which are then used for reconstructing the object surface. Inter-cross section and intra-cross section constraints are imposed to support parameterization and editing of the derived model. Shape and topological differences between adjacent object contours pose several issues for the 3D reconstruction process. By using the contour skeleton information we produce intermediate cross sections representing places where ramifications occur to achieve robust covering (meshing) of adjacent slices. Finally, we present a proof of concept implementation of our method and several examples that demonstrate its effectiveness and efficiency.
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