An ARCore-Based Augmented Reality Campus Navigation System

Veterinary anatomy is an essential course that provides a fundamental basis for various clinical specializations. Artificial intelligence (AI) is increasingly being incorporated into anatomy education, revolutionizing the field through various tools such as 3D-printed models, virtual and augmented reality systems, the Anatomage table, virtual staining techniques, and virtual laboratories. 3D printing has been promoted in the last two decades, and successfully applied in anatomy, surgery and other educational fields. AI-driven virtual dissection platforms are an invaluable resource that provide high-quality anatomical simulations, with a more interactive and immersive learning experience that ensures students receive comprehensive training regardless of their location. Augmented Reality (AR) and Virtual Reality (VR) provide immersive, interactive experiences that significantly improve comprehension of intricate anatomical concepts. Anatomage is a digital dissection table that offers comprehensive anatomy resources for veterinary science for fully segmented, detailed and dissectible dog, cat and other animal scans. Instructors and clinicians can upload their own DICOM files. 3D visualization enables the usage of 3D models in education improves the quality of teaching and the learning outcomes of students with respect to visibility, clarity and quality of the 3D neuro-anatomical models using the XYZ-3D scanner. Virtual dissection might hinder the cultivation of clinical skills in future surgeons by denying students the chance to physically feel and explore the textures of anatomical structures. These technologies can be useful tools in veterinary histology. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. Virtual labs are computer-based simulations that allow students to conduct laboratory experiments online. They save time, reduce costs, and offer a safer alternative to traditional physical labs. Incorporating AI into veterinary anatomy education makes learning more accessible, scalable, and effective for students. AI-driven anatomical tools enable flexible understanding, unconstrained by location or time. Additionally, these resources can accommodate larger groups of students without requiring expanded physical facilities. Moreover, gamified AI learning can lead to higher retention rates and increased student engagement. Thus, AI-based new tools and methods viz., VR, AR, Anatomage table and 3D visualization platforms are revolutionizing anatomy education and can be utilized in teaching and learning of veterinary anatomy to enhance learning experiences to complement rather than replace traditional teaching method.

Recupera algunos de los elementos menos estudiados y reconocidos en el ensayo latinoamericano: su profunda vocación de reflexión, diálogo y mediación intelectual; su plasticidad y apertura a la búsqueda del sentido; su vínculo con la sociabilidad, la conversación y la generación de un espacio simbólico de encuentro; su capa cidad de establecer una nueva retroalimentación entre lectura y escritura.

Augmented reality (AR), an important approach for humans to access more information, has been widely used in various fields, such as medical, military, entertainment, and educational fields. In the medical field, AR can superimpose virtual preoperative patient information on the real intraoperative scene to guide the surgery. Currently, many AR methods are used in the medical field, but most of them require complicated tracking markers or demanding apparatus. This work proposes an AR calibration method with a binocular stereo camera. The accurate feature matching pairs are used to reconstruct the point cloud. Thereafter, the 3D virtual mesh model is superimposed on the real scene by image-patient registration. A head phantom, a liver phantom, and three volunteers participated in our experiments. The accuracy of our method is significantly higher than the other two AR methods.

Achieving seamless integration between virtual objects and real scenes has always been an important issue in augmented reality (AR) research. To achieve this, it is necessary to provide virtual objects with real-time and accurate lighting conditions from a real scene. Therefore, the purpose of this study is to realize lighting consistency rendering for real-time AR systems in outdoor environments, aiming to enhance the user’s sense of immersion. In this paper, we propose a lighting consistency technique for real-time AR systems in outdoor environments based on multi-source geographical information (MGI). Specifically, we introduce MGI into the study of lighting consistency and construct a comprehensive database to store and manage the acquired MGI data. Based on this, we proposed a sky radiance model driven using the MGI. Finally, we utilized the sky radiance model along with light sensor data to render the virtual objects in outdoor scenes. The experimental results show that the shadow angular error is reduced to 5.2°, and the system frame rate is increased to 94.26. This means that our method achieves a high level of realism in the fusion of virtual objects and real scenes while ensuring a high frame rate in the system. With this technology, users can conveniently and extensively realize the lighting consistency rendering of real-time AR systems in outdoor scenes using mobile devices.

In augmented reality, the insertion of virtual objects into the real scene needs to meet the requirements of visual consistency. The virtual objects rendered by the augmented reality system should be consistent with the illumination of the real scene. However, for complex scenes, it is not enough to just complete the illumination estimation. When there are transparent objects in the real scene, the difference in refractive index and roughness of transparent objects will influence the effect of the virtual and real fusion. To tackle this problem, this paper proposes a new approach to jointly estimate the illumination and transparent material for inserting virtual objects into the real scene. We solve for the material parameters of objects and illumination simultaneously by nesting microfacet model and hemispherical area illumination model into inverse path tracing. Although there is no geometry model of light sources in the recovered geometry model, the proposed hemispherical area illumination model can be used to recover scene appearance. Multiple experiments on both virtual and real-world datasets verify that the proposed approach subjectively and objectively performs better than the state-of-the-art method.

As a frontier technology, holography has important research values in fields such as bio-micrographic imaging, light field modulation and data storage. However, the real-time acquisition of 3D scenes and high-fidelity reconstruction technology has not yet made a breakthrough, which has seriously hindered the development of holography. Here, a novel holographic camera is proposed to solve the above inherent problems completely. The proposed holographic camera consists of the acquisition end and the calculation end. At the acquisition end of the holographic camera, specially configured liquid materials and liquid lens structure based on voice-coil motor-driving are used to produce the liquid camera, so that the liquid camera can quickly capture the focus stack of the real 3D scene within 15 ms. At the calculation end, a new structured focus stack network (FS-Net) is designed for hologram calculation. After training the FS-Net with the focus stack renderer and learnable Zernike phase, it enables hologram calculation within 13 ms. As the first device to achieve real-time incoherent acquisition and high-fidelity holographic reconstruction of a real 3D scene, our proposed holographic camera breaks technical bottlenecks of difficulty in acquiring the real 3D scene, low quality of the holographic reconstructed image, and incorrect defocus blur. The experimental results demonstrate the effectiveness of our holographic camera in the acquisition of focal plane information and hologram calculation of the real 3D scene. The proposed holographic camera opens up a new way for the application of holography in fields such as 3D display, light field modulation, and 3D measurement.

In augmented reality system, registration is always a problem which is difficult to resolve effectively, especially for marker less registration. Due to the placed marks for marked visual registration method, it would cause visual pollution, and the registration often fails when the mark is partially occluded. Registered quality directly affects the fusion effects of the virtuality and reality. This paper proposed a marker less tracking registration method combined natural feature points with affine reprojection. Firstly it extracted natural feature points from real scene with SIFT algorithm, then exploited KLT algorithm to track and match the effective feature points in each image, carried out the homography matrix of each frame with affine reprojection technology, and computed the real scene 3D registration for each frame image, so as to implement the tracking registration effectively. This method is also applicable to those cases of plane feature points being occluded. Index Terms - Augmented Reality, Markerless, Tracking, Registration

With the invent of GPS and related technologies, outdoor navigation is simply possible with great accuracy nowadays. But we are still in need of an efficient, reliable and cheaper technology for indoor navigation. In this paper we put forward the idea of an android based application which enables efficient indoor navigation and tracks the exact location of people inside large buildings. Though different technologies like WiFi, Bluetooth, RFID e.t.c, are available for indoor navigation and location tracking, the main problem is that the implementation and maintenance costs are very high. Also, the change in climatic condition affects the working and degrades its performance. In this work, we incorporate the innovative idea of magnetic field identification technology which uses the naturally occurring magnetic fields inside the concrete buildings with simplified magnetic map matching algorithm and the idea from Route boxer algorithm for effective indoor positioning and navigation. A Smart phone with magnetic field sensor and accelerometer enables the application to provide the best indoor navigation facility to the end users.

Cities have started collecting tons of information about themselves using vast sensors arrays and stored in Geographical Information Systems. All this mass of information can already be enriched by citizens through user-generated content. Beyond collecting, abstracting and storing this information raises the problem of its representation. A reasonable assumption is that onsite presentation of the adequate information would provide a good solution. An analogy can be made with the GPS-based navigation systems that merely render a directed arrow depending on location and itinerary. Such a system can be seen as the basis of an augmented reality system. Many projects involving augmented reality in urban environments have emerged in the last 15 years. Yet there remains challenges both on the applications and technological points of views: Outdoor localization and mapping, Data presentation, Interaction and authoring, Application domains, Acceptability, Hardware issues.The goal of this workshop is to review the challenges of urban augmented reality and the available methods that aim at solving some of those challenges and to give the audience of the variety of the existing and yet to invent applications.The workshop will feature 2 full papers about geolocalization from maps and building facade rectification for image-based matching out of 6 submissions. Those papers will appear onto IEEExplore. It will also include a number of applications papers that will directly appear on the workshop website (https://sites.google.com/site/urbanarworkshop/home). It provides a place for discussing the remaining challenges and the solutions the community is trying to bring.

Abstract. 3D Real Scene is an important part of new infrastructure construction, which provides a unified spatial base for economic and social development and informatization of various departments. According to the demand of real 3D Real Scene digital construction, this paper aims to study the method of indoor and outdoor structured monomer reconstruction of city 3D Real Scene, develop digital twin platform, and strive to lead the development of 3D Real Scene. The main research contents of this paper are as follows: 1) Spatio-temporal-spectral-angular remote sensing observation system and data fusion model; 2) Rapid construction method of vector monomer 3D model of indoor and outdoor entity object; 3) 3D structural reconstruction technology of urban component level based on vector images; 4) A universal digital twins platform, LuojiaDT. The technologies we have developed have been widely used in the national 3D Real Scene construction of China, including smart city, smart transportation, cultural heritage protection, public security and police, urban underground pipe network, indoor and outdoor location service. This research will promote the continuous development of digital twins technology.

By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables. Rather than reconstructing geometry, we match, place, and align each object in the scene to thousands of CAD models of objects. In addition to the fully automatic system, the key technical contribution is a novel approach for aligning CAD models to 3D scans, based on deep reinforcement learning. This approach, which we call Learning-based ICP, outperforms prior ICP methods in the literature, by learning the best points to match and conditioning on object viewpoint. LICP learns to align using only synthetic data and does not require ground truth annotation of object pose or keypoint pair matching in real scene scans. While LICP is trained on synthetic data and without 3D real scene annotations, it outperforms both learned local deep feature matching and geometric based alignment methods in real scenes. The proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as well as synthetic scenes of SUNCG. High quality results are demonstrated on a range of real world scenes, with robustness to clutter, viewpoint, and occlusion.

This paper presents live and interactive webware for online learning of computer graphics concepts. A list of demos is provided. Each demo presents a concept in computer graphics by showing a 3D real world scene beside a 2D rendering scene with a list of graphics functions. Each demo allows users to interactively change the parameters and the order of execution of these graphics functions. Changing the parameters of the functions will produce the 2D rendering result from the 3D real world scene. The visual effects of user interaction will be reflected immediately in the 3D real world scene and the 2D rendering result. The webware was written by using the GL4Java library that provides native OpenGL binding for Java. Nate Robin’s well-known demos were implemented. These include translation, projection, light effect, texture mapping, and so on. New demos were also developed with pedagogical considerations in mind to emphasize the differences between model transformation and view transformation. Although the webware is designed for computer graphics learning the methodology is generic and can easily be applied to other disciplines or courses that require heavy visual presentation. This webware reflects our long-term efforts to develop web-based course material to show principles and techniques in computer science in an interactive way. We did this by having the related algorithms run live in the background and allowing students to interact with them in a web browser.

Computational thinking (CT) skills are increasingly important in education to prepare students for the challenges of the digital age. Augmented Reality (AR) and Virtual Reality (VR) have been introduced as immersive technologies that have the potential to enhance CT skills through more interactive learning experiences. However, there is still a gap in understanding the effectiveness of these technologies in supporting the development of CT, particularly in different levels of education and disciplines. Although several studies have highlighted the benefits of AR and VR in education, no systematic review integrates these findings to identify advantages, challenges, and opportunities for further implementation. Therefore, this study conducted a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by analyzing 25 empirical studies (AR=17, VR=8) obtained from the Scopus database (2008-2024). The analysis addresses four key research questions: (1) the current state of AR/VR in CT development, (2) their advantages, (3) implementation challenges, and (4) future research directions. The results show that AR is more widespread than VR at various levels of education, with dominance in higher education followed by secondary and primary schools. Computer science is the main field of application of AR and VR, while AR is also widely applied in mathematics to increase interest and problem-solving. A total of 11 studies reported significant impacts of these technologies on CT, with AR being superior in increasing student motivation and engagement, as well as aiding in problem-solving and debugging. In contrast, VR provides a more immersive learning experience by strengthening concept understanding, especially in programming and recursion. However, several obstacles in the application of AR and VR, such as hardware limitations, costs, and user skills, affect the effectiveness of these technologies in the learning environment. This study also identified potential future research, including the exploration of VR in primary and kindergarten education, the application of VR in non-computer science fields, and the efficient use of these technologies in supporting the CT process. This study provides more precise insights into the optimal ways of utilizing AR and VR in developing CT skills. It is a reference for educators, policymakers, and researchers in supporting CT learning.

Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some approaches rely on dedicated hardware, such as magnetic trackers or infra-red cameras, but they are too expensive and cumbersome to reach a large public. Others are based on specifically designed markers which usually look like bar-codes. However, they alter the look of objects to be augmented, thereby hindering their use in application for which visual design matters. Recent advances in Computer Vision have made it possible to track and detect objects by relying on natural features. However, no such method is commonly used in the AR community, because the maturity of available packages is not sufficient yet. As far as deformable surfaces are concerned, the choice is even more limited, mainly because initialization is so difficult. Our main contribution is therefore a new AR framework that can properly augment deforming surfaces in real-time. Its target platform is a standard PC and a single webcam. It does not require any complex calibration procedure, making it perfectly suitable for novice end-users. To satisfy to the most demanding application designers, our framework does not require any scene engineering, renders virtual objects illuminated by real light, and let real elements occlude virtual ones. To meet this challenge, we developed several innovative techniques. Our approach to real-time registration of a deforming surface is based on wide-baseline feature matching. However, traditional outlier elimination techniques such as RANSAC are unable to handle the non-rigid surface's large number of degrees of freedom. We therefore proposed a new robust estimation scheme that allows both 2–D and 3–D non-rigid surface registration. Another issue of critical importance in AR to achieve realism is illumination handling, for which existing techniques often require setup procedures or devices such as reflective spheres. By contrast, our framework includes methods to estimate illumination for rendering purposes without sacrificing ease of use. Finally, several existing approaches to handling occlusions in AR rely on multiple cameras or can only deal with occluding objects modeled beforehand. Our requires only one camera and models occluding objects at runtime. We incorporated these components in a consistent and flexible framework. We used it to augment many different objects such as a deforming T-shirt or a sheet of paper, under challenging conditions, in real-time, and with correct handling of illumination and occlusions. We also used our non-rigid surface registration technique to measure the shape of deformed sails. We validated the ease of deployment of our framework by distributing a software package and letting an artist use it to create two AR applications.

Navigation is a coordinated process of movement and wayfinding. This concept is commonly tied to outdoor navigational tasks like finding a place or indoor navigational chores such as locating a product within a store. Focusing on the challenges of indoor navigation, revealed that shoppers sometimes encounter difficulty locating items or become disoriented, which may lead to frustration. This study proposed a conceptual model for alleviating challenges with indoor navigation. The model was prototyped as two main components - navigation and recognition, working in conjunction with each other. The navigation component applied the A* search algorithm and was simulated using the paper prototyping methodology. On the other hand, the recognition component was developed as a mobile Augmented Reality (AR) application. Within the context of a supermarket, the prototype was compared against the conventional shopping method to determine efficiency. Furthermore, the prototype was assessed for usability, user reactions and experiences, strengths and weaknesses. Key findings revealed that most shoppers enjoyed interacting with the prototype, due to its novel design and efficiency in finding items. This was an indication that AR is indeed a useful advancement in the area of indoor navigation.