Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging “hands-on” training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.

Monocular Simultaneous Localization and Mapping (SLAM), Visual Odometry (VO), and Structure from Motion (SFM) are techniques that have emerged recently to address the problem of reconstructing objects or environments using monocular cameras. Monocular pure visual techniques have become attractive solutions for 3D reconstruction tasks due to their affordability, lightweight, easy deployment, good outdoor performance, and availability in most handheld devices without requiring additional input devices. In this work, we comprehensively overview the SLAM, VO, and SFM solutions for the 3D reconstruction problem that uses a monocular RGB camera as the only source of information to gather basic knowledge of this ill-posed problem and classify the existing techniques following a taxonomy. To achieve this goal, we extended the existing taxonomy to cover all the current classifications in the literature, comprising classic, machine learning, direct, indirect, dense, and sparse methods. We performed a detailed overview of 42 methods, considering 18 classic and 24 machine learning methods according to the ten categories defined in our extended taxonomy, comprehensively systematizing their algorithms and providing their basic formulations. Relevant information about each algorithm was summarized in nine criteria for classic methods and eleven criteria for machine learning methods to provide the reader with decision components to implement, select or design a 3D reconstruction system. Finally, an analysis of the temporal evolution of each category was performed, which determined that the classical-sparse-indirect and classical-dense-indirect categories have been the most accepted solutions to the monocular 3D reconstruction problem over the last 18 years.

Bite mark analysis assumes the uniqueness of the dentition can be accurately recorded on skin or an object. However, biting is a dynamic procedure involving three moving systems, the maxilla, the mandible, and the victim's reaction. Moreover, bite marks can be distorted by the anatomic location of the injury or the elasticity of the skin tissue. Therefore, the same dentition can produce bite marks that exhibit variations in appearance. The complexity of this source of evidence emphasizes the need for new 3D imaging technologies in bite mark analysis. This article presents a new software package, DentalPrint® (2004, University of Granada, Department of Forensic Medicine and Forensic Odontology, Granada, Spain) that generates different comparison overlays from 3D dental cast images depending on the pressure of the bite or the distortion caused by victim-biter interaction. The procedure for generating comparison overlays is entirely automatic, thus avoiding observer bias. Moreover, the software presented here makes it impossible for third parties to manipulate or alter the 3D images, making DentalPrint suitable for bite mark analyses to be used in court proceedings.