Impact Score 7.42
Video Computation and Reconstruction in Digital Twins
Image and video are important channels for people to obtain information in modern society. With the advancement of communication and computing technology, multimedia technology centered around video and image application has become an indispensable part of the information society and has deeply integrated into people's daily production and life. The traditional video and image are displayed through two-dimensional plane, losing the depth information of three-dimensional (3D) scene, but the world in people's eyes is a 3D space. For a long time, people have been dreaming of a 3D display of the real world to get immersive visual impact and feelings. With the continuous development of computer technology and the updating of digital equipment, more and more fields have a higher accuracy demand for 3D model reconstruction. There are three methods for the 3D model reconstruction of scene content. (1) Mathematical modeling or geometric modeling technology can establish models with manual object measurement and computer-aided design. This kind of model has a smooth surface and no noise, but it consumes a lot of time and labor. (2) Laser scanning equipment can establish a model with all-around scanning, which has higher accuracy, more realistic texture features, but high equipment cost. (3) Computer vision technology can recover the spatial geometric information of the scene or object from the image or video. However, the establishment of a 3D model with modeling software through manual measurement consumes much time and labor. Researchers have been focusing on how to obtain the 3D model of the object directly and quickly through an algorithm. The 3D model reconstructed through the algorithm still has some shortcomings, such as slow reconstruction speed and uneven quality. Thus, the 3D reconstruction of large outdoor scenes based on video images is a hot research direction.
Digital Twins refers to the establishment and simulation of a physical entity, process, or system in an information platform. With Digital Twins, the state of physical entities can be understood on the information platform, and the predefined interface components in physical entities can be controlled. Digital Twins is a concept in the IoT. A digital simulation model is established in the information platform through the integration of physical feedback data, AI (Artificial Intelligence), ML (Machine Learning), and software analysis. This simulation model automatically changes with the change of physical entities according to feedback. Ideally, Digital Twins can self-learn according to multiple feedback source data and present the real situation of physical entities in the digital world in nearly real-time. The concept of Digital Twins is first proposed to describe the manufacturing and real-time virtualization of products. The concept of Digital Twins has then been further developed thanks to the improvement of the sensing technology, hardware and software technology, and computer calculation performance. Moreover, Digital Twins has also been applied in satellite monitoring, optimization, management, and control , based on a deep fusion technology of remote sensing data and a dynamic real-time modeling and evaluation technology of the system. The satellite’s Digital Twins can be constructed at the ground station using the telemetry data downloaded from the satellite in near-real time, which can provide information about the health status of the satellite , thus enabling predictive maintenance.. The satellite state is comprehensively analyzed and calculated in depth through abundant and enhanced sensing information of the mathematical model, which is presented to the user a comprehensive and detailed satellite state monitoring interface. The Digital Twins is realized based on the following technologies: high-performance computing, advanced sensing acquisition, digital simulation, intelligent data analysis, VR (Virtual Reality) rendering, and super-realistic image rendering of the target physical entity. Digital Twins can describe the health state of the target entity perfectly and meticulously, and the deep, multi-scale, and probabilistic dynamic state can be assessed through the integration of data and physics.
At present, the implementation of Digital Twins in video computing and video reconstruction depends on a large variety of technology challenges, which they can be identified through four layers that start from the basic data acquisition layer to the top application layer, namely, the data support layer, the modeling computing layer, the Digital Twins function layer, and the immersion experience layer. The realization of each layer is based on the previous layers, which further enriches and expands the functions of the previous layers. The aim of this special issue is to provide readers with a comprehensive overview of the application of the Digital Twins technology in the field of video computing and video reconstruction. Especially, related research on the functional layer of Digital Twins. Meanwhile, original works and comment articles are welcomed here, as well as opinions, methods, and modeling research.
The topics may include but are not limited to:
Full Paper Submission: December 20, 2021
Decisions on Acceptance/ Rebuttal: February 20, 2022
Final Papers Submission: April 20, 2022
The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: Digital Twins Video”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Professor, North China Sea Offshore Engineering Survey Institute, Ministry Of Natural Resources North Sea Bureau, Qingdao, China
Email: [email protected]
Senior Researcher, Fondazione Bruno Kessler, Italy
Email: [email protected]
Associate Professor, School of Computing and Information Technology, University of Wollongong, Australia
Email: [email protected]