The web has been an extremely effective collaboration platform, enabling services like Wikipedia article co-authoring, blogging, social messaging, video conferencing, and many others. However, the collaboration should ideally occur Peer to Peer (P2P) among the participants instead of going through a centralized server as in the current centralized web, which acts as a mediator as well as a repository of data, especially for face-to-face collaboration in 3D XR context. Most notable XR applications like MMORPG have been developed in a dedicated application platform with their own centralized game servers. Nowadays, the decentralized web is being promoted as the next web architecture in numerous fronts such as blockchain in cryptocurrency, reviving P2P storage, and networking technologies as the next web, Web 3.0. It would be beneficial if we could make an XR collaboration framework based on the recent developments of the decentralized web. This paper explores one possible amalgamation of the decentralized web technology stack toward a webized XR collaboration framework.
A collaborative Virtual Reality System (VRS) software with X3D interactive visualization has been developed for the RAPID Code System. The software is named “VRS-RAPID”.
VRS-RAPID has several key features including an intuitive and fast input preparation; on-the-fly simulation of nuclear systems using RAPID; 3D, 2D, and tabular visualization of RAPID outputs; nuclear inspection and detection capabilities; and possibility for collaboration of users at different sites. The latter feature provides an excellent capability for training of students and professionals.
Different layers of X3D models are used for the visualization of the nuclear system and to display the code output and the system environment. VRS-RAPID can be accessed via a web browser from a variety of devices, including computers, tablets, and smart-phones. The current version of VRS-RAPID includes databases for simulation of Spent Nuclear Fuel (SNF) Pools, SNF Casks, and nuclear reactor cores. The RAPID code system is developed based on the innovative Multi-stage Response-function Transport (MRT) methodology, that has been thoroughly benchmark and allows for real-time simulation of nuclear systems.
Although developed for coupling with the nuclear code system RAPID, the VRS can be adapted to the modeling and simulation of any physics. The goal of the VRS system is to enable real-time computation and analysis of real-life nuclear systems in a collaborative setting for professionals and students, even far apart from each other; as well as simplifying the input preparation and output processing of complex scientific computing software.
In this paper, we present and evaluate a web service that offers cloud-based machine learning services to improve Augmented Reality applications on mobile and web clients with special regards to tracking quality and registration of complex scenes that require an application-specific coordinate frame. Specifically, our service aims at reducing camera drift that still occurs in modern AR frameworks as well as helps with the initial camera alignment in a known scene by estimating the absolute camera pose using a configurable context-based image segmentation in combination with an adaptive image classification. We demonstrate real-world applications that utilize our web service and evaluate the performance and accuracy of the underlying image segmentation and the camera pose estimation. We also discuss the initial configuration along with the semi-automatic process of generating training data, and the training of the machine learning models for the corresponding tasks.
For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it. While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This allows general deployment of computer vision algorithms on the web, which was previously not possible due to lack of calibration data.
In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data. For a typical example of a point cloud with 0.7 million points per 3D frame at 30 fps, the point cloud raw video needs a bandwidth around 500MB/s. Thus, efficient compression methods are mandatory for ensuring the storage/transmission of such data, which include both geometry and attribute information. In the last years, the issue of 3D point cloud compression (3D-PCC) has emerged as a new field of research. In addition, an ISO/MPEG standardization process on 3D-PCC is currently on-going. In this paper, a comprehensive overview of the 3D-PCC state-of-the-art methods is proposed. Different families of approaches are identified, described in details and summarized, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data.
A large number of sensors deployed in recent years in various setups and their data is readily available in dedicated databases or in the cloud. Of particular interest is real-time data processing and 3D visualization in web-based user interfaces that facilitate spatial information understanding and sharing, hence helping the decision making process for all the parties involved.
In this research, we provide a prototype system for near real-time, continuous X3D-based visualization of processed sensor data for two significant applications: thermal monitoring for residential/commercial buildings and nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are sparsely placed, in each application, where they collect data for large periods (of up to one year), we employ a Finite Differences Method and a Neural Networks model to approximate data distribution in the entire volume.
We present an approach for classifying 3D point clouds of indoor environments using Convolutional Neural Network (CNN)-based image analysis for entropy-selected 3D views. Prior to classification, the 3D point clouds are clustered using either Density-based Spatial Clustering of Applications with Noise (DBSCAN) or k-means algorithms. We then use randomly sampled 3D point normal vectors as an entropy descriptor to calculate the direction and position of the virtual camera, which is placed around these clusters. It synthesizes 2D images of a given cluster from multiple views with positions and directions that have highest visual entropy. We then proceed to classify the images using a retrained CNN. We test our approach for classifying common office furniture items using both synthetic and actual 3D point clouds of typical indoor office spaces. The empirical results demonstrate that our approach is suited towards classifying specific indoor furniture objects. We also describe how our approach can be implemented as a lightweight component within a service-oriented system that is used for visualization and classification of 3D point clouds using a given Web3D tool. The resulting semantically-enriched 3D point clouds can further be used for digital indoor environment representations, with further use as base data for Building Information Modeling (BIM), Facility Management (FM), and Digital Twin (DT) applications.
This paper introduces a web application for point cloud annotation that is used in the advanced driver assistance systems field. Apart from the point cloud viewer, the web tool has an object viewer and a timeline to define the attributes of the annotations and a video viewer to validate the point cloud annotations with the corresponding video images. The paper also describes several strategies we followed to obtain annotations correctly and quickly: (i) memory management and rendering of large-scale point clouds, (ii) coherent combination of video images and annotations, (iii) content synchronization in all parts of the application and (iv) automatic annotation before and during the annotation task of the user.
Social media in virtual reality is in a high-growth market segment with influential products and services in virtual tourism, remote education, and business meetings. Nevertheless, previous systems have never achieved an online platform which renders a 6DoF mirrored world with geotagged social media in real time. In this paper, we introduce the technical detail behind Geollery.com which reconstructs a mirrored world at two levels of detail. Given a pair of latitude and longitude coordinates, our pipeline streams and caches depth maps, street view panoramas, and building polygons from Google Maps and OpenStreetMap APIs. At a fine level of detail for close-up views, we render textured meshes using adjacent local street views and depth maps. When viewed from afar, we apply projection mappings to 3D geometries extruded from building polygons for a coarse level of detail. In contrast to teleportation, our system allows users to virtually walk through the mirrored world at the street level. Our system integrates geotagged social media from both internal users and external sources such as Twitter, Yelp, and Flicker. We validate our real-time strategies of Geollery.com on various platforms including mobile phones, workstations, and head-mounted displays.
We introduce a coding framework that supplements introductory computer graphics courses, with the goal of teaching graphics fundamentals more effectively and lowering the excessive barrier of entry to 3D graphics programming. In particular, our framework provides tiny-graphics.js, a new WebGL-based software library for implementing projects, including an improved organization system for graphics code that has greatly benefited our students. To mitigate the difficulty of creating 3D graphics-enabled websites and online games, we furthermore introduce the “Encyclopedia of Code”—a World Wide Web framework that encourages visitors to learn 3D computer graphics, build educational graphical demos and articles, host them online, and organize them by topic. Our own contributed examples include various interactive tutorials and educational games. Some of our modules expose students to new graphics techniques, while others explore new modes of online learning, collaboration, and computing. In comparison to earlier online graphics coding platforms and mainstream graphics educational materials, the resources that we have developed offer a significantly unique set of features for both inside and outside our classrooms.
We introduce a novel method for rectangular selection of components in large 3D models on the web. Our technique provides an easy to use solution that is developed for renderers with partial fragment shader support such as embedded systems running WebGL. This method was implemented using the Unity 3D game engine within the 3D Repo open source framework running on a web browser. A case study with industrial 3D models of varying complexity and object count shows that such a solution performs within reasonable rendering expectations even on underpowered devices without a dedicated graphics card.
A methodical modeling of human respiratory organ is created by using the respiratory modeling architecture (RMA) of the scene-graph of H-Anim structure. H-Anim follows an X3D file format to construct the modeling features for the level of detail of a complex human respiratory organs including structures, inner surfaces, and the lungs. The respiratory animation can be created by the respiratory structures and keyframe animation. The generated animation offers the motions of how human lungs, ribcage, and diaphragm perform during their breathing. Moreover, to visualize the respiratory modeling and animation on the Web, a web standard method of X3DOM framework is utilized for interactive display of 3D scenes.
Recent advances in 3D imaging techniques allow detailed representations of the anatomy of animals and are an important tool in comparative morphology. However, the volume data are rather large and require special programs for visualization. A web based interface is created to allow inspection of volume data for everybody directly in a browser. We currently host more than 6000 CT datasets; each of it can be inspected and sub-volumes can be requested and viewed in a high resolution within one second response time. Our system offers direct visualization of high resolution volume data which hopefully will find broad applications in science and education.
360-degree cinematic virtual reality(CVR) could bring users a full perspective experience, but high freedom will cause the loss of important information. To bring the user back to the narrative thread, researchers have made a variety of guided attempts, such as using note reminders, voice guidance, and so on. However, these methods are less efficient and less controllable for CVR for the purpose of science education. To solve this issue, the authors proposed visual guidance technology by synchronizing camera animation and 360-degree video in VR. When the user does not operate the mouse, the CVR viewing angle is controlled by the camera animation synchronously. This technology effectively transmits important information while allowing users to interact with the 360 video freely, thus improving the cognitive effect in science education.
The paper is a case study for the effectiveness of hand-held and head-mounted AR interaction for increasing teaching and learning, in the specific field of marine ecology education. This system will serve as an assisting tool for the teaching activity of marine ecology guide to improve the learning effectiveness of the learners. A total of 191 questionnaires were collected, there were 161 valid questionnaires, among which including 70 AR questionnaires, 91 AR2VR questionnaires. There were 30 incomplete invalid questionnaires. This study adopts the pre-test and post-test variance design of the quasi-experimental research method to explore the learners’ flow experience effect, technology acceptance model, activity involvement and learning effect under different guidance modes, as well as their attitudes towards the use and acceptance of the guidance system. The experiments are performed and analyzed in detail. The study results indicate that (a) the learning achievements of the students differed between before and after the AR and VR mobile guidance activities, (b) both the AR and VR guidance modes enhanced the flow experience of visitors, (c) all aspects of the technology acceptance models in both groups had positive effects on the flow experience, (d) all aspects of the activity involvement of both groups had positive effects on flow experience, and (e) most learners exhibited positive attitudes toward and acceptance of using AR and VR mobile guidance systems.