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Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR. Firstly, we propose a heuristic mechanism for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Secondly, we introduce a motion-aware key frame selection mechanism to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

The internet-of-things (IoT) refers to the growing field of interconnected pervasive computing devices and the networking that supports smart, embedded applications. The IoT has multiple human-computer interaction challenges due to its many formats and interlinked components, and central to these is the need to provide sensory information and situational context pertaining to users in a more human-friendly, easily understandable format. This work addresses this by applying mixed reality toward expressing the underlying behaviors and states internal to IoT devices and IoT-enabled objects. It extends the authors’ previous research on IoT Avatars (mixed reality character representations of physical IoT devices), presenting a new head-mounted display framework and interconnection architecture. This contributes i) an exploration of mixed reality for smart spaces, ii) an approach toward expressive avatar behaviors using fuzzy inference, and iii) an early functional prototype of a hybrid physical and mixed reality IoT-enabled object. This approach is a step toward new information presentation, interaction, and engagement capabilities for smart devices and environments.

During crisis situations, emergency operators require fast information access to achieve situation awareness and make the best possible decisions. Augmented reality could be used to visualize the wealth of user-generated content available on social media and enable context-adaptive functions for emergency operators. Although emergency operators agree that social media analytics will be important for their future work, it poses a challenge to filter and visualize large amounts of social media data. We conducted a goal-directed task analysis to identify the situation awareness requirements of emergency operators. By collecting tweets during two storms in Germany we evaluated the usefulness of Twitter data for achieving situation awareness and conducted interviews with emergency operators to derive filter strategies for social media data. We synthesized the results by discussing how the unique interface of augmented reality can be used to integrate social media data into emergency control rooms for situation awareness support.

© 2018 by the authors. Collaboration has been common in workplaces in various engineering settings and in our daily activities. However, how to effectively engage collaborators with collaborative tasks has long been an issue due to various situational and technical constraints. The research in this paper addresses the issue in a specific scenario, which is how to enable users to interact with public information from their own perspective. We describe a 3D mobile interaction technique that allows users to collaborate with other people by creating a symmetric and collaborative ambience. This in turn can increase their engagement with public displays. In order to better understand the benefits and limitations of this technique, we conducted a usability study with a total of 40 participants. The results indicate that the 3D mobile interaction technique promotes collaboration between users and also improves their engagement with the public displays.

Egocentric augmented reality (AR) interfaces are quickly becoming a key asset for assisting high precision activities in the peripersonal space in several application fields. In these applications, accurate and robust registration of computer-generated information to the real scene is hard to achieve with traditional Optical See-Through (OST) displays given that it relies on the accurate calibration of the combined eye-display projection model. The calibration is required to efficiently estimate the projection parameters of the pinhole model that encapsulate the optical features of the display and whose values vary according to the position of the user's eye. In this article, we describe an approach that prevents any parallax-related AR misregistration at a pre-defined working distance in OST displays with infinity focus; our strategy relies on the use of a magnifier placed in front of the OST display, and features a proper parameterization of the virtual rendering camera achieved through a dedicated calibration procedure that accounts for the contribution of the magnifier. We model the registration error due to the viewpoint parallax outside the ideal working distance. Finally, we validate our strategy on a OST display, and we show that sub-millimetric registration accuracy can be achieved for working distances of ±100 mm around the focal length of the magnifier.

Augmented reality (AR) applications that overlay a user's perception of the real world with digitally generated information are on the cusp of commercial viability. AR has appeared in several commercial platforms like Microsoft HoloLens and smartphones. They extend the user experience beyond two dimensions and supplement a user's normal 3D world. A typical location-based multi-player AR application works through a three-step process, wherein the system collects sensory data from the real world, identifies objects based on their context, and finally, renders information on top of the user's senses. However, because these AR applications frequently exchange data with users, they have exposed new individual and public safety issues. In this paper, we develop ARSpy, a user location tracking system solely based on network traffic information of the user, and we test it on location-based multi-player AR applications. We demonstrate the effectiveness of the proposed scheme via real-world experiments on 12 volunteers and show that we could obtain the geolocation of any target with high accuracy. We also propose three defense methods to mitigate these side channel attacks. Our results reveal a potential security threat in location-based multi-player AR applications and serve as a critical security reminder to AR users.

In the aviation industry, cable bracket is one of the most common parts. The traditional assembly state inspection method of cable bracket is to manually compare by viewing 3D models. The purpose of this paper is to address the problem of inefficiency of traditional inspection method. In order to solve the problem that machine learning algorithm requires large dataset and manually labeling of dataset is a laborious and time-consuming task, a simulation platform is developed to automatically generate synthetic realistic brackets images with pixel-level annotations based on 3D digital mock-up. In order to obtain accurate shapes of brackets from 2D image, a brackets recognizer based on Mask R-CNN is trained. In addition, a semi-automatic cable bracket inspection method is proposed. With this method, the inspector can easily obtain the inspection result only by taking a picture with a portable device, such as augmented reality (AR) glasses. The inspection task will be automatically executed via bracket recognition and matching. The experimental result shows that the proposed method for automatically labeling dataset is valid and the proposed cable bracket inspection method can effectively inspect cable bracket in the aircraft. Finally, a prototype system based on client-server framework has been developed for validation purpose.

This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone's guiding images with the Green Zone's view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

To help individuals deal with volleyball learning and training, an online volleyball remote teaching system is designed based on Augmented Reality (AR) technology. Firstly, the action images of the volleyball coach were captured, followed by being input into the computer. The hardware of the system was mainly composed of motion capture equipment, data transmission equipment, central control processing equipment, and AR display equipment. The system’s software included four modules of motion acquisition and recognition. The testing results showed that the functional modules and performance of the system could meet the expected requirements, verifying the effectiveness and practicability.

The visual representation of a product and the role of visualization have recently become a central issue in design research. By enriching a real scene with computer generated objects, Augmented Reality, has proven itself as a valuable Human-Computer Interface in numerous application areas, such as medicine, military, entertainment and manufacturing. In this paper we propose an approach by which a user can create own 3D augmented reality scenes that enable interaction between the real world and virtual assembly's components, while including an animation at the same time. For this purpose, BuildAR Pro software is employed using marker-based camera tracking, while assembly design is obtained with standard CAD system SolidWorks. The animations are developed in 3ds max software package in order to save the assembly as .ive file format, which is helpful to increase the performance of scene rendering and/or viewing.

Point cloud is an essential format for three-dimensional (3-D) object modelling and interaction in Augmented Reality and Virtual Reality applications. In the current state of the art video-based point cloud compression (V-PCC), a dynamic point cloud is projected onto geometry and attribute videos patch by patch, each represented by its texture, depth, and occupancy map for reconstruction. To deal with occlusion, each patch is projected onto near and far depth fields in the geometry video. Once there are artifacts on the compressed two-dimensional (2-D) geometry video, they would be propagated to the 3-D point-cloud frames. In addition, in the lossy compression, there always exists a tradeoff between the rate of bitstream and distortion. Although some geometry-related methods were proposed to attenuate these artifacts and improve the coding efficiency, the interactive correlation between projected near and far depth fields has been ignored. Moreover, the non-linear representation ability of Convolutional Neural Network has not been fully considered. Therefore, we propose a learning-based approach to remove the geometry artifacts and improve the compressing efficiency. We have the following contributions. We devise a two-step method working on the near and far depth fields decomposed from geometry. The first stage is learning-based Pseudo-Motion Compensation. The second stage exploits the potential of the strong correlations between near and far depth fields. Our proposed algorithm is embedded in the V-PCC reference software. To the best of our knowledge, this is the first learning-based solution of the geometry artifacts removal in V-PCC. The extensive experimental results show that the proposed approach achieves significant gains on geometry artifacts removal and quality improvement of 3-D point-cloud reconstruction compared to the state-of-the-art schemes.

One key predictor of team performance and productivity is the teams’ ability to coordinate its subtasks as precisely as possible over time. Thereby, the quality of the temporal coordination in teams is highly dependent on several cognitive team skills, like for instance the ability to build a precise and stable mental model of the situation (shared situation awareness) and of the team task process (task state awareness/ TSA). To support these cognitive skills in teams, various methods and trainings already exist. However, considering teams that work de-located, the prerequisites and therefore the requirements for team supportive methods change. This work presents the results of an empirical usability and user experience (UX) study of an interface for an Augmented Reality-based assistance system for spatially dispersed teams, called Ambient Awareness Tool (AAT). The AAT interface consists of graphical information about the teamwork process and aims at enhancing the TSA of the team, thereby supporting its temporal coordination. Within the framework of a participatory design process we conducted a two-part expert-user-study, comprising a laboratory experiment for the evaluation of the interface usability and a follow-up online survey to additionally investigate the UX. By means of the usability scores we first inferred three interface configuration clusters. A subsequent UX evaluation then allowed us to designate the configuration cluster with the highest usability and UX scores. As a result, we defined one interface configuration that will be investigated concerning its actual impact on the temporal coordination of spatially dispersed teams in a further study.

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

Today, neuronavigation is widely used in daily clinical routine to perform safe and efficient surgery. Augmented reality (AR) interfaces can provide anatomical models and preoperative planning contextually blended with the real surgical scenario, overcoming the limitations of traditional neuronavigators. This study aims to demonstrate the reliability of a new-concept AR headset in navigating complex craniotomies. Moreover, we aim to prove the efficacy of a patient-specific template-based methodology for fast, non-invasive, and fully automatic planning-to-patient registration. The AR platform navigation performance was assessed with an in-vitro study whose goal was twofold: to measure the real-to-virtual 3D target visualization error (TVE), and assess the navigation accuracy through a user study involving 10 subjects in tracing a complex craniotomy. The feasibility of the template-based registration was preliminarily tested on a volunteer. The TVE mean and standard deviation were 1.3 and 0.6 mm. The results of the user study, over 30 traced craniotomies, showed that 97% of the trajectory length was traced within an error margin of 1.5 mm, and 92% within a margin of 1 mm. The in-vivo test confirmed the feasibility and reliability of the patient-specific template for registration. The proposed AR headset allows ergonomic and intuitive fruition of preoperative planning, and it can represent a valid option to support neurosurgical tasks.

Whilst research of immersive technology has been growing, substantial efforts thus far have been scattered. Conflicting ideas and arguments from scholars provoke uncertainty and questions about the validity of these outputs in fostering more extensive implementation of immersive technology in the construction industry. Consequently, the direction for further developments in research remains unclear. This study aims to identify the status quo of immersive technology adoption in construction. A systematic review with thematic analysis was conducted. Common themes and concerns about the use of immersive technology in the construction are classified under the framework of SWOT (strengths, weaknesses, opportunities and threats). The results indicate that whilst ‘strengths’ and ‘opportunities’ display a broad array of applications and functions that are derived from real benefits, a substantial amount of opportunities mentioned are driven by optimism. Alternatively, the results from ‘weaknesses’ and ‘opportunities’ identify several limitations involved in the uptake of immersive technology in the construction industry. Findings of this study provide vision that can help direct resources to those measures that can best meet the needs of construction industry. It is suggested that future resources are better to be put on integration with proven effective systems like Building Information Modelling (BIM) and Computer Aided Design (CAD) software. New developments should due consider the real demand of the industry.

The construction industry is prone to major safety hazards owing to the complex and onsite nature of construction projects. Hence, construction researchers have been pursuing concepts, methods, and tools using advancing technologies to improve construction safety management. Subsequently, the rapid digitization of construction work practices is providing opportunities to improve construction safety. Research has revealed that construction safety management practices can benefit from the applications of virtual design construction technologies (VDC), such as building information modeling (BIM), virtual reality (VR), augmented reality (AR), geographic information systems (GIS), and gaming technology. This study comprehensively reviews 191 research articles, published between 2010 and 2019, focusing on providing an overview of the implementation and application of VDC technologies for improving construction safety. The objective of this review is to critically collect and analyze applications of VDC technologies and present a holistic view of the features and functions of VDC technologies that can impact jobsite safety improvement in the construction industry. The review showed that VDC technologies can substantially improve construction safety. Emerging digital technologies, such as BIM, VR, AR, GIS, and gaming technologies can transform the traditional document-oriented safety procedures into digitalized safety practices allowing safety managers to visualize and analysis construction sites virtually to devise proactive safety measures and effective safety trainings. This study also highlights challenges such as research gaps regarding these digital tools that are currently impeding their widespread use in construction safety.

FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.

PurposeOver the years, technology development has rationalized supply chain processes. The demand economy is disrupting every sector causing the supply chain to be more innovative than ever before. The digitalization of the supply chain fulfils this demand. Several technologies such as blockchain, big data analytics, 3D printing, Internet of things (IoT), artificial intelligence (AI), augmented reality (AR), etc. have been innovated in recent years, which expedite the digitalization of the supply chain. The paper aims to analyse the applicability of these technological enablers in the digital transformation of the supply chain and to present an interpretive structural modelling (ISM) model, which presents a sequence in which enablers can be implemented in a sequential manner.Design/methodology/approachThis paper employed the ISM approach to propose a various levelled model for the enablers of the digital supply chain. The enablers are also classified graphically based on their driving and dependence powers using matrix multiplication cross-impact applied to classification (MICMAC) analysis.FindingsThe study indicates that the enablers “big data analytics”, “IoT”, “blockchain” and “AI” are the most powerful enablers for the digitalization of the supply chain and actualizing these enablers should be a topmost concern for organizations, which want to exploit new opportunities created by these technologies.Practical implicationsThis study presents a systematic approach to adopt new technologies for performing various supply chain activities and assists the policymakers better organize their assets and execution endeavours towards digitalization of the supply chain.Originality/valueThis is one of the initial research studies, which has analysed the enablers for the digitalization supply chain using the ISM approach.

Measurement of joint range of motion is a common measure in the functional evaluation of a patient. This clinical measurement is performed through the use of mechanical goniometry, currently presenting various problems mainly of a human nature. This article introduces ROMCam, an alternative system for measuring joint range of motion, based on estimating the human pose in 2D. For this, use is made of artificial vision libraries and the use of an RGB webcam type camera. The results obtained corroborate the validity of the use of ROMCam as a low cost, accessible tool that can even be used as a resource in telerehabilitation treatments.

PurposeThe paper aims to analyze the application areas of augmented reality (AR) in the construction industry to enhance its usage level. The objectives are to assess the level of awareness of hardware and software devices of AR, examine the application areas of AR and reveal lagging areas in the construction industry.Design/methodology/approachA survey of construction professionals engaged in the built environment was sent a well-structured questionnaire in the study area. The professionals involved are project managers, architects, engineers, builders and quantity surveyors. The sampling technique used in selecting those professionals was convenience sampling. Descriptive and inferential statistics were used in analyzing the retrieved data.FindingsThe five most applied areas of Augmented Realities are Visualization and simulation of construction works; Project documentation; Project planning, monitoring and modification. The other two areas include on-site real-time information retrieval and health and safety measures. The findings also revealed the difference in the opinions of professionals among the variables. The results showed that there is significant difference in the opinions of the professionals regarding the application areas except one, that is automated measurement. Automated measurement has the same converging opinions of all professionals engaged in the study.Originality/valueThe study gives deep insight into possible areas where AR can be used in construction. The awareness level of hardware and software devices of AR was revealed by showing that those devices are upcoming in their usage. The areas of application of AR in construction are in their nascent stage. When there is proper implementation of this technology it will improve management in construction, minimize health and safety issues, and enhance the efficiency of workers through visualization and simulation.

This paper presents the evaluation of Augmented Reality Application development for the educational purpose of Palembang cultural artefacts in Sultan Mahmud Badaruddin II (SMB II) Museum. The method applied in this study effectively developed an AR application to visualize the 3D object of SMB II Museum collections and created new digital 3D objects of Palembang cultural artefact which no longer exist. The digitization of the collection is an innovation that provides new culture learning experiences utilizing AR technology and also contributes to the preservation of Palembang cultural artefacts. Users can interact directly with these artefacts virtually which cannot be performed in the real environment but AR enabling it. In term of effectiveness and efficiency, some testing scenarios were performed. The results showed that the application could run its functionality as designed with time responsiveness for detecting marker was 0.599 seconds on average. App usability was also evaluated using the SUS method. The SUS survey score of 78.3 showed that the developed AR of the SMB II museum was acceptable by two variations of the millennial user. The augmented reality (AR) application is proposed as a medium to support the services of SMB II museum for education that effective improving users knowledge by 28.5% after using the App. The use of AR in the SMB II Museum leads to an enhanced interactive learning experience that promotes cultural heritage preservation in Palembang Indonesia.

As the scaling down of Si devices in the range less than few nm has been expedited up to a physical limit of Si, low dimensional materials have been regarded as one of next generation semiconductors. Among a variety of applications, studies on photodetectors have been actively investigated with their novel optical properties as well as astonishing electrical properties. However, most of research has focused on single device-type photodetector (i.e., photo-diode or photo-transistor). Contrary to common photodetector, light-to-frequency circuits (LFCs) are based on frequency reading with photosensitive ring oscillators, which has better noise immunity and reduced system complexity, thus, can be utilized to novel application even in internet of things (IoT) and bio & medical fields. In this review, low dimensional materials based circuit level photodetectors, which are core elements as the form of either inverters or ring oscillators for demonstration of LFCs, are introduced. Along with the introduction of low dimensional materials and their optical properties for optoelectronics, a fundamental concept for LFCs is specifically described. Thereafter, research progress on low dimensional material based photosensitive inverters is addressed according to the types of devices. Furthermore, as one of practical method for the improvement of photodetector performance, molecular doping technology is presented. Lastly, complete system of LFCs and its digitization for demonstration of production level, and potential application in the respective four aspects, (i) medical SpO2 detection, (ii) biological fluidic system, (iii) auto-lighting in agriculture, and (iv) optical feedback and sensing systems, are presented as systematic way to address the envisioned practical applications for the future displays including virtual reality and augmented reality, and others. As a remark, LFCs based on low dimensional semiconductors are expected to be one of core components in trillion’s sensor area.

On-vehicle AR-HUD (head-up display) is a driving assistant system, which can let drivers see navigation and warning information directly through the windshield with an easy-to-perceive augmented reality form. Traditional AR-HUD can only produce the content at a specific distance, which causes uncomfortable experiences such as frequent refocusing and unnatural floating of virtual objects. This paper proposed an innovative AR-HUD system, which can provide stereoscopic scenes to the driver. The system is composed of two traditional HUD displays and supports parallax by additive light field factorization. Optical paths and illumination of two displays are precisely calibrated for both views to ensure the combination of lights is as expected. The factorization algorithm is optimized for the system. With GPU acceleration, the system can run in real time. The system is cheap and simple to transform from a traditional AR-HUD system, which presents a feasible scheme to achieve a fusion-enhanced augmented reality assistant for driving.

Among the existing platforms, the mobile platform provides most of the augmented reality experiences for users. Augmented reality solutions have been applied in several contexts due to technological advances in this area. However, many of these advances are still out of reach of more massive access by regular users due to access limitations or the cost of these new technologies. After conducting a literature review considering the application of data visualization in augmented reality environments, we have identified an evolution of equipment used in this context with a decrease of low-cost proposals. Hence, the works related to these two areas showed high use of technologies not yet accessible to the vast majority of users. Thus, we propose a low-cost augmented reality application that can be used on mobile devices for data visualization. We developed a hybrid proposal of an augmented reality prototype to interacting with cards through the mobile device to support the process of creating data visualization. The tangible cards have a design that helps the user in the visualization creation process, indicating each card’s role in the interaction process. The proposed application supports a multi-user environment, allowing multiple users to interact simultaneously with data visualizations in the AR environment. The visualizations are generated on a dedicated server for this function, providing a set of seven different types of visualization. To validate the developed prototype we performed two tests, the first being a stress assessment on the server that generates data visualizations to analyze the proposed application’s scalability. The second was a remote usability test with twelve participants, to evaluate the interaction and design of the cards, and the viability of this solution for augmented reality applications.

This paper investigates the art design of user interaction experience in mobile systems through the methods of contextual perception and machine learning. The theoretical foundations for the design of intangible cultural heritage interactive display resources include digital display theory, intangible cultural heritage education theory, embodied cognition theory, and gamification design theory. Based on the modelling and analysis of the theoretical foundations, the design principles are derived, including the heritage education principle, the somatic interaction design principle, and the content design principle. In this paper, we use ontologies to construct user knowledge models, fuse multi-situational similarity metrics, screen out candidate neighbour sets through preliminary screening, then combine the user's activity to construct time-based weight tensor scores, use tensor decomposition to obtain recommendation evaluation values, and finally use the recommendation evaluation values to make artistic recommendations. The experimental results show that the algorithm can still obtain a good recommendation implementation in the case of extremely sparse data. The analysis of the fit between augmented reality and folk art appreciation class, as well as the reference to relevant application cases, make design and practice of augmented reality application in folk art appreciation class, try to solve the common problems in folk art appreciation class, analyse the feedback effect and make a summary and outlook.

PurposeThe physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.Design/methodology/approachThe viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.FindingsResults show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.Research limitations/implicationsSuggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.Originality/valueThis study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

Virtual and augmented reality allows the utilization of natural user interfaces, such as realistic finger interaction, even for purposes that were previously dominated by the WIMP paradigm. This new form of interaction is particularly suitable for applications involving manipulation tasks in 3D space, such as CAD assembly modeling. The objective of this paper is to evaluate the suitability of natural interaction for CAD assembly modeling in virtual reality. An advantage of the natural interaction compared to the conventional operation by computer mouse would indicate development potential for user interfaces of current CAD applications. Our approach bases on two main elements. Firstly, a novel natural user interface for realistic finger interaction enables the user to interact with virtual objects similar to physical ones. Secondly, an algorithm automatically detects constraints between CAD components based solely on their geometry and spatial location. In order to prove the usability of the natural CAD assembly modeling approach in comparison with the assembly procedure in current WIMP operated CAD software, we present a comparative user study. Results show that the VR method including natural finger interaction significantly outperforms the desktop-based CAD application in terms of efficiency and ease of use.

Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.

The current pandemic crisis has highlighted the utmost importance of communication networks. The dramatic development of teleworking imposed by strict confinement measures, the need to access educational materials from home, the likely degradation of social relationships are among the impacts of the pandemic that inevitably affect the operation and the performance of networks. The design and the operation of tomorrow’s networks will have to learn from the outbreak that exacerbates the critical need for massive digital inclusion, robust and resilient infrastructures, as well as dynamically adaptive networking to better cope with various, possibly degraded, network access conditions. Of course, the virus is not the only reason to rethink network design: current 5G deployments are among the networking technologies that encourage the emergence of new services, such as immersive services based upon Augmented Reality/Virtual Reality techniques that are pretty demanding in terms of QoS. This industry vision paper provides a network operator’s perspective on some of the forthcoming networking challenges. It explores a few technical options that are likely to be instrumental in the conception and the delivery of optimized, automated and protective networks, for the benefits of the end-user, regardless of his/her connectivity capabilities, whether he/she is in motion or not, and regardless of the nature of the service or contents he/she needs to access.

While augmented and virtual reality technologies are becoming mainstream, it is still technically challenging and time-consuming to create new applications. Many designers draw from traditional low-fidelity prototyping methods that do not lend themselves well to designing in 3D. Developers use high-end programming frameworks such as Unity and Unreal which require significant hardware/software setups and coding skills. We see a gap in the medium-fidelity range where there is an opportunity for new tools to leverage the advantages of 360° content for AR/VR prototyping. Existing tools, however, have only limited support for 3D geometry, spatial and proxemic interactions, puppeteering, and storytelling. We present 360theater, a new method and a tool for rapid prototyping of AR/VR experiences, which takes dioramas into the virtual realm by enhancing 360° video capture with 3D geometry and simulating spatial interactions via Wizard of Oz. Our comparative evaluation of techniques with novice and experienced AR/VR designers shows that 360theater can close the gap and achieve a higher fidelity and more realistic AR/VR prototypes than comparable methods.

Publisher Copyright: © 2021 ACM. Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm∗14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.

Light-on-dark color schemes, so-called “Dark Mode,” are becoming more and more popular over a wide range of display technologies and application fields. Many people who have to look at computer screens for hours at a time, such as computer programmers and computer graphics artists, indicate a preference for switching colors on a computer screen from dark text on a light background to light text on a dark background due to perceived advantages related to visual comfort and acuity, specifically when working in low-light environments. In this article, we investigate the effects of dark mode color schemes in the field of optical see-through head-mounted displays (OST-HMDs), where the characteristic “additive” light model implies that bright graphics are visible but dark graphics are transparent . We describe two human-subject studies in which we evaluated a normal and inverted color mode in front of different physical backgrounds and different lighting conditions. Our results indicate that dark mode graphics displayed on the HoloLens have significant benefits for visual acuity and usability, while user preferences depend largely on the lighting in the physical environment. We discuss the implications of these effects on user interfaces and applications.

High angular resolution light field (LF) enables exciting applications such as depth estimation, virtual reality, and augmented reality. Although many light field angular super-resolution methods have been proposed, the reconstruction problem of LF with a wide-baseline is far from being solved. In this paper, we propose an end-to-end learning-based approach to achieve angular super-resolution of the light field with a wide-baseline. Our model consists of three components. We first train a convolutional neural network to predict the depth map for each sub-aperture view. Then the estimated depth maps are used to warp the input views. In the final component, we first use a convolutional neural network to fuse the initial warped light fields, and then we propose an edge-aware inpainting network to modify the inaccurate pixels in the near-edge regions. Accordingly, we design an EdgePyramid structure that contains multi-scale edges to perform the inpainting of near-edge pixels. Moreover, we introduce a novel loss function to reduce the artifacts and further estimate the similarity in near-edge regions. Experimental results on various light field datasets including large-baseline light field images show that our method outperforms the state-of-the-art light field angular super-resolution methods, especially in the terms of visual performance near edges.

Many modern national libraries implement the model of a library open to any member of society. In conditions of rapid and pervasive development of digital technologies and the formation of the Internet information space, a website is an indispensable tool for ensuring the availability of the library, its information resources and services in the virtual space. The creation and use of websites to organize remote (virtual) services is one of the most significant activities of libraries. The article objective is to present and summarize the results of studying national library websites as a platform for remote user services. The Russian State Library researchers study websites of 24 national libraries in 22 countries as a part of investigating current state and prospects for development of the digitalization processes of public libraries. The researchers have identified main directions of remote services (informing users about the library, its resources and services; ensuring the availability of library information resources; improving the comfort of using the library), and the range of remote services, which includes above dozen remote library and information services, as well as nearly the same number of additional digital services that increase the comfort of users. They proposed promising directions to develop remote services (information about library resources and services, consulting and training on using library resources, personalization of services), and the introduction of digital technologies (multimedia and augmented reality technologies).

Our augmented reality online assistance platform enables an expert to specify 6DoF movements of a component and apply the geometrical and physical constraints in real-time. We track the real components on the expert’s side to monitor the operations of an expert. We leverage a remote rendering technique that we proposed previously to relieve the rendering burden of the augmented reality end devices. By conducting a user study, we show that the proposed method outperforms conventional instructional videos and sketches. The answers to the questionnaires show that the proposed method receives higher recommendation than sketching, and, compared to conventional instructional videos, is outstanding in terms of instruction clarity, preference, recommendation, and confidence of task completion. Moreover, as to the overall user experience, the proposed method has an advantage over the video method.

With the development of augmented reality supporting manual assembly collaboration, it is particularly important to explore the transformation from traditional “human-machine” cooperation mode to smart “human-machine” cooperation mode. Early studies have shown that user cues (i.e., head, gesture, eye) and scene cues (i.e., objects, tools, space) are intuitive and highly expressive for traditional AR collaborative mode. However, how to integrate these cues into an assembly system, reasonably infer an operator’s work intention, and then give an appropriate rendering scheme is one of the problems in collaborative assembly. This paper describes a AR collaborative assembly platform: SHARIDEAS. It uses a generalized grey correlation method to integrate user cues and scene cues. The results of data fusion can provide appropriate and intuitive assembly guidance for local workers. A formal user study is to explore the usability and feasibility of SHAREDEAS in a manual assembly task. The experimental data show that SHAREDEAS is more conducive than traditional one to improve the efficiency of human-machine cooperation. Finally, some conclusions of SHARIDEAS are given and the future research direction has prospected.

Zusammenfassung In dieser Arbeit stellen wir einen neuen Ansatz zur geometrischen Inspektion großer mechanischer Bauteile vor. Dazu benutzen wir ein Portal, das auf einer Linearachse manuell verschoben werden kann. An dem Portal sind ein frei beweglicher 3D-Streifenlichtsensor und eine zusätzliche Augmented-Reality-Kamera angebracht. Insgesamt deckt das Portal einen Arbeitsraum von 8 , 0 × 3 , 0 × 0 , 8 m 3 8,0\times 3,0\times 0,8\hspace{0.1667em}{\text{m}^{3}} ab, wobei die Position und Orientierung der Sensoren in einem globalen Koordinatensystem durch ein optisches Motion-Capturing-System bestimmt werden. Es wird gezeigt, dass Nutzer mittels Augmented Reality sehr schnell und intuitiv grobe Geometrieabweichungen erkennen können. Zur detaillierten, metrischen Vermessung können 3D-Punktewolken aufgenommen und automatisiert ausgewertet werden.

Quality control procedures are essential in many industrial production pipelines. These repetitive and precise tasks are frequently complex, including several steps that must be performed correctly by different operators. To facilitate these, quality control tests are often documented with static media like video recordings, photos, or diagrams. However, the need for the operator to divide attention between the visual instructions and the task, and the lack of feedback lead to slow processes, with potential for improvement. By using augmented reality (AR), operators can focus on the task at hand while receiving visual feedback where it is needed. Nevertheless, existing prototypes are still at early stages, being tested only in laboratory conditions, far from mimicking real scenarios. The major contributions of this work are twofold: first, we present an AR-based quality control system capable of generating virtual content to guide operators by overlaying information in a video stream while performing real-time validation. The system evaluates the current status of the procedure to ensure the automatic progression to the next phase. Second, an evaluation was conducted in an industrial shop floor during 1 week, with seven operators to verify if the system was robust and understand possible efficiency gains when compared to the alternative, i.e., video instructions. Results showed that AR had a significant impact in procedures’ execution time (reduction of 36%), while reducing the risk of human errors, which means AR technologies may represent a profitable and sustainable solution when applied to real-world industrial scenarios, in the long run.

Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10$\%$ and 24$\%$ increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets., Code available at https://bitbucket.org/RSKothari/ellseg/src/master/

Smart vehicles are expected to be equipped with high-dimensional, resource-intensive applications, including platoon control, augmented reality supported gaming, AI-based pedestrian detection, fuel scheduling, and so on, catering to diverse user preferences and enhancing safety and efficiency. These applications pose unique challenges for resource-constrained vehicles due to their intense computation requirements, whereas vehicular edge computing (VEC) networks, consisting of roadside units (RSUs) and MEC servers, contain the capability of providing cloud-like computing experience at vehicular edges while meeting performance requirements in terms of latency and throughput. Moreover, the development of intelligent VEC (IVEC) infrastructure is accelerated due to rapid advancement of AI algorithms in recent years. However, IVEC is prone to attacks, including fake computation feedback, unfair or biased resource allocation in a VEC server, and so on, due to its centralized governance and black box computation (edge computation works like a black box for end users). To combat such security vulnerabilities, we propose a blockchain-based decentralized architecture to enhance transparency in IVEC resource management and leverage edge consumers (e.g., vehicles) with a computation verification option. Additionally, we address the unbalanced load distribution issue and propose a secure IVEC federation model for balancing loads. We also outline the main challenges and provide a brief description of promising research directions to draw the attention of concerned stakeholders and parties in both the blockchain and edge computing domains.

In this letter, we propose a novel joint mobile cache and power management scheme for energy-efficient mobile augmented reality (MAR) services in mobile edge computing (MEC). For this purpose, depending on whether the cache is hit or not, analytical models for 1) the energy consumption of an MAR device (MD) and 2) the service latency for the MAR are derived and investigated. Considering a tradeoff between energy and service latency in terms of the cache size in MAR with MEC, we design a theoretical framework for the mobile cache management of the MD to optimize the mobile cache size as well as transmission power while guaranteeing the required service latency. From the numerical experiments, we evaluate the performance of the proposed scheme and demonstrate insights regarding the optimization of the performance of MEC-assisted MAR services with a mobile cache.

Image-based relighting, projector compensation and depth/normal reconstruction are three important tasks of projector-camera systems (ProCams) and spatial augmented reality (SAR). Although they share a similar pipeline of finding projector-camera image mappings, in tradition, they are addressed independently, sometimes with different prerequisites, devices and sampling images. In practice, this may be cumbersome for SAR applications to address them one-by-one. In this paper, we propose a novel end-to-end trainable model named DeProCams to explicitly learn the photometric and geometric mappings of ProCams, and once trained, DeProCams can be applied simultaneously to the three tasks. DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses: shading attributes estimation, rough direct light estimation and photorealistic neural rendering. A particular challenge addressed by DeProCams is occlusion, for which we exploit epipolar constraint and propose a novel differentiable projector direct light mask. Thus, it can be learned end-to-end along with the other modules. Afterwards, to improve convergence, we apply photometric and geometric constraints such that the intermediate results are plausible. In our experiments, DeProCams shows clear advantages over previous arts with promising quality and meanwhile being fully differentiable. Moreover, by solving the three tasks in a unified model, DeProCams waives the need for additional optical devices, radiometric calibrations and structured light., Source code and supplementary material at: https://github.com/BingyaoHuang/DeProCams

Simultaneous Localization and Mapping (SLAM) systems are proposed to estimate robot poses and reconstruct 3-D map of surrounding environment. Since most of the existing SLAM systems rely on the static-world assumption, to perform them in dynamic environments still remains challenging. In this paper, we propose a novel sparse feature based visual SLAM, named as DP-SLAM, which is based on a moving probability propagation model for dynamic keypoints detection. The probability indicates the likelihood of one keypoint being located on the moving objects. Our approach combines the results of geometry constraints and semantic segmentation to track the dynamic keypoints in a Bayesian probability estimation framework. We integrate our method into the front-end of the ORB-SLAM2 system, which acts as a pre-processing stage to filter out keypoints that are associated with moving objects. Furthermore, we inpaint the frame background that has been occluded by the detected dynamic objects, which benefit some applications such as virtual and augmented reality. Experimental results on the TUM RGB-D dataset and our own sequences demonstrate that our approach can improve performance of state-of-the-art SLAM system in various challenging scenarios.

This article presents a personalized mixed reality (MR) surgical assistance system for brachytherapy. Using a novel, modified multi-information fusion method, the fusion of virtual organs and a preoperative plan for an actual patient and the real-time tracking of surgical tools were achieved. Using the quaternion-based iterative closest point (QICP) algorithm and a hand-eye calibration method, the preoperative plan can be fused into individual patients. Using the electromagnetic (EM) tracker, users can track the surgery tools in real time, without multiple CT scans, and doctors can immediately perform the surgery. We performed a series of experiments, including phantom and animal experiments, to test the accuracy and efficiency of the system. In the phantom experiment, the average needle location error was 0.957 mm. Based on the results of animal experiments, the needle insertion error was 2.416 mm. All experimental results indicated that the procedure could be applied in further clinical studies.

Depth estimation is a classic task in computer vision, which is of great significance for many applications such as augmented reality, target tracking and autonomous driving. Traditional monocular depth estimation methods are based on depth cues for depth prediction with strict requirements, e.g. shape-from-focus/ defocus methods require low depth of field on the scenes and images. Recently, a large body of deep learning methods have been proposed and has shown great promise in handling the traditional ill-posed problem. This paper aims to review the state-of-the-art development in deep learning-based monocular depth estimation. We give an overview of published papers between 2014 and 2020 in terms of training manners and task types. We firstly summarize the deep learning models for monocular depth estimation. Secondly, we categorize various deep learning-based methods in monocular depth estimation. Thirdly, we introduce the publicly available dataset and the evaluation metrics. And we also analysis the properties of these methods and compare their performance. Finally, we highlight the challenges in order to inform the future research directions.

Augmented reality ( AR ) is important for training complex tasks, such as navigation, assembly, and medical procedures. The effectiveness of such training may depend on accurate spatial localization of AR objects in the environment. This article presents two experiments that test egocentric distance perception in augmented reality within and at the boundaries of action space (up to 35 m) in comparison with distance perception in a matched real-world ( RW ) environment. Using the Microsoft HoloLens, in Experiment 1, participants in two different RW settings judged egocentric distances (ranging from 10 to 35 m) to an AR avatar or a real person using a visual matching measure. Distances to augmented targets were underestimated compared to real targets in the two indoor, RW contexts. Experiment 2 aimed to generalize the results to an absolute distance measure using verbal reports in one of the indoor environments. Similar to Experiment 1, distances to augmented targets were underestimated compared to real targets. We discuss these findings with respect to the importance of methodologies that directly compare performance in real and mediated environments, as well as the inherent differences present in mediated environments that are “matched” to the real world.

PurposeThis paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions to use MRDT. The factors are used as a set of key metrics for proposing a predictive model for virtual, augmented and mixed reality (MR) acceptance by users. This model is called the extended TAM for MRDT adoption in the architecture, engineering, construction and operations (AECO) industry.Design/methodology/approachAn interpretivist philosophical lens was adopted to conduct an inductive systematic and bibliographical analysis of secondary data contained within published journal articles that focused upon MRDT acceptance modelling. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to meta-analysis were adopted to ensure all key investigations were included in the final database set. Quantity indicators such as path coefficients, factor ranking, Cronbach’s alpha (a) and chi-square (b) test, coupled with content analysis, were used for examining the database constructed. The database included journal papers from 2010 to 2020.FindingsThe extant literature revealed that the most commonly used constructs of the MRDT–TAM included: subjective norm; social influence; perceived ease of use (PEOU); perceived security; perceived enjoyment; satisfaction; perceived usefulness (PU); attitude; and behavioural intention (BI). Using these identified constructs, the general extended TAM for MRDT in the AECO industry is developed. Other important factors such as “perceived immersion” could be added to the obtained model.Research limitations/implicationsThe decision to utilise a new technology is difficult and high risk in the construction project context, due to the complexity of MRDT technologies and dynamic construction environment. The outcome of the decision may affect employee performance, project productivity and on-site safety. The extended acceptance model offers a set of factors that assist managers or practitioners in making effective decisions for utilising any type of MRDT technology.Practical implicationsSeveral constraints are apparent due to the limited investigation of MRDT evaluation matrices and empirical studies. For example, the research only covers technologies which have been reported in the literature, relating to virtual reality (VR), augmented reality (AR), MR, DT and sensors, so newer technologies may not be included. Moreover, the review process could span a longer time period and thus embrace a fuller spectrum of technology development in these different areas.Originality/valueThe research provides a theoretical model for measuring and evaluating MRDT acceptance at the individual level in the AECO context and signposts future research related to MRDT adoption in the AECO industry, as well as providing managerial guidance for progressive AECO professionals who seek to expand their use of MRDT in the Fourth Industrial Revolution (4IR). A set of key factors affecting MRDT acceptance is identified which will help innovators to improve their technology to achieve a wider acceptance.

Purpose of Review The aim of this case report was to illustrate the clinical procedure integrating augmented reality (AR) for complex patient cases requiring full mouth rehabilitation. Recent Findings The introduction of AR technology to the fields of medicine and dentistry has led to numerous applications in education, surgery, and esthetics. Recently, a new AR software was introduced in esthetic dentistry which allows for real-time smile projection and thus improves communication with patients and the dental laboratory. Summary The presented case shows a patient with multiple missing teeth, diastemata, and an impaired masticatory and phonetic ability. After reconstruction of the posterior zone, the AR software was used for the conception of the esthetic zone, integrating the patient into the decision-making process. The result was an esthetic rehabilitation applying palatal and buccal veneers which corresponded to the chosen AR design. The patient appreciated the opportunity to pre-visualize a possible final outcome in an interactive way which increased his confidence in the chosen treatment. Further studies are needed to assess the precision and reproducibility of the described protocol.

With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveying and mapping work. This article mainly studies the application of remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. First, perform grayscale processing on the image in the experiment, then remove the noise in the image and smooth the image through the median filter method and finally use the Canny operator to perform edge detection to obtain a binarized image containing only the target object, and this is done by image feature extraction. After using three-dimensional scanning modeling to extract the image feature points, the target manager is used for sample analysis. Obtain the projection matrix through the interface, and then perform coordinate conversion to complete the positioning of the target scene. In this paper, the BRISK feature point detection algorithm with fast speed and small calculation is used to detect the target, and SVM is used for remote sensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remote sensing image processing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.

The development of massive IoT has not only brought about a wealth of hardware resources but also brought about the problems of difficult data management, resource running and low efficiency. The emergence of sixth-generation (6G) network will not only provide faster data rates, more device connections but also bring ubiquitous virtual reality/augmented reality (VR/AR) services. In the 6G era, large-scale IoT devices will generate VR/AR service and resource requirements, and the network will also face unprecedented pressure to respond to the ubiquitous VR/AR requirements. To address the above issues, this article proposes the information-centric massive Internet of Things (IC-mIoT) suitable for 6G large-scale VR/AR content distribution to improve the efficiency of IC-mIoT and fully guarantee the Quality of Service (QoS) of users. First, this article introduces the blockchain for IC-mIoT nodes and proposes a new consensus mechanism Proof-of-Cache-Offloading (PoCO). Second, an architecture using blockchain-enabled IC-mIoT for VR/AR is proposed in this article. The massive IoT resources are fully integrated and scheduled to support large-scale VR/AR applications and IC-mIoT. Third, a Stackelberg game model and a cache index selection and calculation algorithm are formulated for blockchain-enabled cache offloading. The analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.