In high-rise buildings, complex layouts, and frequent structural modifications can make emergency rescues challenging. Conventional 2D rescue plans offer no real-time guidance and can be difficult for rescuers to interpret under stress. To overcome these limitations, we introduce an advanced Mixed Reality (MR) application designed for real-time rescue assistance in multi-floor buildings. Built for the Meta Quest 3, a cost-efficient standalone MR headset, our system enables users to scan, update, and navigate a dynamic 3D model of their surroundings. We implement external data storage and utilize spatial anchors to ensure accurate realignment to bypass the Meta Quest 3’s constraint of storing only 15 rooms. Additionally, the application utilizes the A* algorithm to dynamically calculate optimal routes based on the user’s real-time location, taking into account the room layout and any obstacles inside. Users can navigate using either a floating 3D minimap or a 2D minimap anchored to their left hand, with staircases seamlessly incorporated into navigation routes, including virtual door warnings and automatic removal of navigation cues near stairs for safety. To improve user experience, we have implemented hand tracking for interactions. We conducted a study with a large sample of participants to evaluate usability and effectiveness. This study included the NASA Task Load Index (NASA-TLX) to assess the cognitive load, along with the System Usability Scale (SUS), the Self-Assessment Manikin (SAM), the Single Ease Question (SEQ), task completion times, and a post-evaluation feedback questionnaire. The results demonstrate that the system achieves high usability, low cognitive workload, and positive user experience while supporting situational awareness, user confidence, and efficient navigation. The results indicate that a system based on an MR headset has the potential to improve situational awareness and decision-making in dynamic indoor environments.

Integrating Virtual Reality (VR) and Human–Computer Interaction (HCI) has transformed user engagement with virtual environments, enhancing immersion and usability. Technologies like Cave Automatic Virtual Environment (CAVE) and Head-Mounted Displays (HMDs) have shown significant promise in visualizing data, especially for examining and comprehending intricate 3D datasets, such as graph visualizations. To explore the effectiveness of these technologies in data visualization, we conducted a user study comparing user experience and performance across these two systems when interacting with a large 3D graph. The virtual environment and interaction modalities were adapted to each platform: the HMD setup utilized dual 6-DOF controllers, while the CAVE configuration employed a Flystick2 controller and a trackball. Preliminary data on participants’ demographics, motion sickness sensitivity, and prior experience with graph theory were collected to provide context for the findings. Results show that users in the HMD condition reported significantly higher levels of perceived presence and involvement, as well as improved task performance in navigation and interaction tasks. While both systems were rated similarly for perceived usefulness and ease of use, the HMD environment offered a more immersive and emotionally positive experience overall. These findings contribute to immersive analytics research by demonstrating the comparative strengths of HMD-based systems for individual 3D graph exploration, while highlighting the potential advantages of CAVE for low-discomfort settings. The study underscores the importance of aligning system design with user profiles and task demands to optimize data exploration in virtual environments.

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%.

Three-dimensional (3D) modeling of non-linear objects from stylized sketches is a challenge even for computer graphics experts. The extrapolation of object parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we propose a broker system that can transform a stylized sketch of a tree into a complete 3D model by mediating between a modeler and a 3D modeling software. The input sketches do not need to be accurate or detailed: They must only contain a rudimentary outline of the tree that the modeler wishes to 3D model. Our approach is based on a well-defined Deep Neural Network architecture, called TreeSketchNet (TSN), based on convolutions and capable of generating Weber and Penn [ 1995 ] parameters from a simple sketch of a tree. These parameters are then interpreted by the modeling software, which generates the 3D model of the tree pictured in the sketch. The training dataset consists of synthetically generated sketches that are associated with Weber–Penn parameters, generated by a dedicated Blender modeling software add-on. The accuracy of the proposed method is demonstrated by testing the TSN with synthetic and hand-made sketches. Finally, we provide a qualitative analysis of our results, by evaluating the coherence of the predicted parameters with several distinguishing features.

Abstract Advanced intelligent surveillance systems are able to automatically analyze video of surveillance data without human intervention. These systems allow high accuracy of human activity recognition and then a high-level activity evaluation. To provide such features, an intelligent surveillance system requires a background subtraction scheme for human segmentation that captures a sequence of images containing moving humans from the reference background image. This paper proposes an alternative approach for human segmentation in videos through the use of a deep convolutional neural network. Two specific datasets were created to train our network, using the shapes of 35 different moving actors arranged on background images related to the area where the camera is located, allowing the network to take advantage of the entire site chosen for video surveillance. To assess the proposed approach, we compare our results with an Adobe Photoshop tool called Select Subject, the conditional generative adversarial network Pix2Pix, and the fully-convolutional model for real-time instance segmentation Yolact. The results show that the main benefit of our method is the possibility to automatically recognize and segment people in videos without constraints on camera and people movements in the scene (Video, code and datasets are available at http://graphics.unibas.it/www/HumanSegmentation/index.md.html ).

Virtual reality has achieved significant popularity in recent years, and allowing users to move freely within an immersive virtual world has become an important factor critical to realize. The user’s interactions are generally designed to increase the perceived realism, but the locomotion techniques and how these affect the user’s task performance still represent an open issue, much discussed in the literature. In this article, we evaluate the efficiency and effectiveness of, and user preferences relating to, freehand locomotion techniques designed for an immersive virtual environment performed through hand gestures tracked by a sensor placed in the egocentric position and experienced through a head-mounted display. Three freehand locomotion techniques have been implemented and compared with each other, and with a baseline technique based on a controller, through qualitative and quantitative measures. An extensive user study conducted with 60 subjects shows that the proposed methods have a performance comparable to the use of the controller, further revealing the users’ preference for decoupling the locomotion in sub-tasks, even if this means renouncing precision and adapting the interaction to the possibilities of the tracker sensor.

In the Social Web age, the role of the web user has evolved from a simple consumer of web content to the main actor who interacts with other users, shares data and cooperates in social networks, online communities, blogs, wikis, feeds, and chats. His opinions, comments, and suggestions have an amazing influence on the online communities and users that can be inadvertently influenced in decision-making activities such as buying a certain product or trusting the recommendations of the blog, etc. Big corporations, as well as scientific communities, study the user behavior trying to capture human feeling and emotions, aimed at guessing the “client” preferences and then attend his expectations. Emotion extraction using natural language is a complex activity that needs to understand the content and capture the sentiment hidden in the written text. To this end, the work proposes a text analysis based on Deep Learning (DL) to capture the emotions that regulate human feeling in the natural language. The work shows the effectiveness of this approach presenting a comparative analysis of emotion-based text classification by DL neural networks methods, with different datasets and feature settings.

This work examines the use of virtual reality to visualize and interact with three-dimensional graphs. We developed the design to be as natural and intuitive as possible, through analysis, study, and use of several layout algorithms, which allow the nodes of a graph to be positioned to reduce entropy levels. We chose to use this schematic visualization of graphs because it can describe the graphical data synthetically and the links between the data while presenting the data in a visual format. The application was developed entirely using Unreal Engine version 4, and the visualization was performed using the head-mounted display HTC Vive.

This paper examines the application of a deep learning approach to converting night-time images to day-time images. In particular, we show that a convolutional neural network enables the simulation of artificial and ambient light on images. In this paper, we illustrate the design of the deep neural network and some preliminary results on a real indoor environment and two virtual environments rendered with a 3D graphics engine. The experimental results are encouraging and confirm that a convolutional neural network is an interesting approach in the fields of photo-editing and digital image postprocessing.

We present a client-server framework for the development of mobile applications that use Augmented Reality (AR) to visualize geolocated data. Geo-information displays allow users to understand and respond effectively to the context in which the application is deployed. We provide a scalable and flexible architecture for the development and management of the client, the server and the data that are used by the applications. This architecture is based on the display of connected layers that represent structured information. The approach has been implemented in two case studies: the management of failures in electrical power lines, and to support hydrogeological monitoring.

We present an approach based on a forest metaphor to ease the comprehension of evolving object oriented software systems. The approach takes advantages of familiar concepts such as forests of trees, sub-forest of trees, trunks, branches, leaves, and color of the leaves. In particular, each release of a software is represented as a forest that users (or software maintainers) can navigate and interact with. Users can pass from a release to another one, so understanding how the entire software and its classes evolve throughout the past releases. The approach has been implemented in a prototype of a 3D interactive environment. A preliminary empirical evaluation has been also conducted to assess that environment and the underlying approach.