Search Results (823)

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment. Full article

3D scene reconstruction serves as a fundamental technology with widespread applications in virtual reality, structural inspection, and robotic systems. While recent advances in 3D Gaussian Splatting have significantly enhanced scene reconstruction capabilities, the performance of such methods remains suboptimal when applied to highly reflective environments. To overcome this limitation, we introduce ARS-GS, a novel framework that integrates Anisotropic Spherical Gaussian reflection modeling and spherical harmonics diffuse approximation into a physically based rendering pipeline. This architecture incorporates a skip connection between the Anisotropic Spherical Gaussian module and the Gaussian primitives, effectively preserving surface details while maintaining computational efficiency. Comprehensive experimental evaluations validate the efficacy of ARS-GS across multiple datasets. Specifically, our method establishes new state-of-the-art quantitative benchmarks, achieving a peak signal-to-noise ratio of 38.30 and a structural similarity index measure of 0.997 on the neural radiance fields synthetic dataset, alongside a peak signal-to-noise ratio of 46.31 on the Gloss Blender dataset. Furthermore, on the challenging reflective neural radiance fields real-world dataset, our approach secures the highest peak signal-to-noise ratio scores, highlighted by a metric of 26.26 on the Sedan scene. The proposed method also substantially reduces perceptual errors, yielding a learned perceptual image patch similarity as low as 0.204, thereby consistently outperforming existing techniques in the reconstruction of highly specular surfaces with superior geometric fidelity. Full article

Recent deep exploration in the northern Sichuan Basin has advanced our understanding of Lower Cambrian Canglangpu Formation carbonate reservoirs. However, the characteristics, genesis, and distribution of the reservoir, as well as future exploration targets, remain unclear. Specifically, core and thin-section analyses indicate that these reservoirs are notably tight, with virtually no visible macroporosity and low permeability (0.01–1 mD). However, helium porosity measurements reveal values of 2–5%, suggesting significant storage potential. An integrated approach utilizing optical and scanning electron microscopy (SEM), high-pressure mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR), and micro-computed tomography (micro-CT) was employed to characterize the pore systems. Quantitative thin-section analysis reveals visible areal porosity markedly lower than helium porosity, indicating predominance of micropores; mercury intrusion and NMR demonstrate that intragranular and intergranular micropores constitute most pore volume, although effectively connected throat sizes remain below 1 µm. Comparative stratigraphic evaluations show that porosity is more developed in the dolomite-rich upper and middle intervals of the depositional cycles, whereas the lower intervals are less porous. Early subaerial exposure promoted dolomitization and dissolution, which facilitated pore development. However, the influence of sediment mixing led to a reduction in porosity. And deep burial subjected the rocks to intense compaction and cementation, destroying most of the primary pore space. Consequently, reservoir quality is ultimately governed by the interplay between the original depositional environment and the later diagenetic history, with paleotopographic highs identified as the most promising exploration targets. These findings establish a predictive framework for reservoir quality in tight carbonate rocks, which holds significant implications for analogous plays worldwide. Full article

Distance learners in higher education are often assumed to face limited peer interaction, potentially weakening their sense of belonging. This study examines peer relationships and belonging among students in distance and blended university programs, with attention to the role of virtual reality (VR) within digitally mediated learning environments. Immersive VR teaching is included in the curriculum for distance learning students in the studied programs. Using a mixed-methods design, survey data and open-ended responses were collected from 17 students in Information Studies and Information Service Design. An adapted Classroom Community Scale was supplemented with items addressing the perceived contribution of different communication technologies. Contrary to expectations, fully distance learners did not report weaker agreement with statements reflecting belonging than blended students; on several items, they expressed stronger agreement, particularly regarding perceived peer support and learning opportunities. Results indicate that conventional 2D communication tools, particularly chats and video calls, are central to sustaining peer relationships. VR was not perceived as essential but described by some students as an added value supporting shared experience and group cohesion. Overall, belonging emerges as a socio-technical achievement shaped by communication practices rather than physical proximity. Full article

In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R² = 0.9091; 5-fold cross-validation R² = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications. Full article

Immersive virtual environments are increasingly investigated as tools capable of modulating conscious experience, yet the specific contribution of graded immersion to altered states of consciousness (ASC), time perception, and cognition remains unclear. The present study examined how different levels of immersion during videogame play influence subjective experience and post-experience cognitive performance. Seventy-two participants played an identical 35 min segment of the videogame Half-Life: Alyx under one of three conditions: desktop PC (low immersion), head-mounted virtual reality (VR; medium immersion), or VR combined with full-body locomotion via an omnidirectional treadmill (high immersion). Following gameplay, participants completed validated measures of presence (IPQ), immersion (IEQ), ASC (5D-ASC), retrospective time estimation, and cognitive flexibility (Stroop task and Alternative Uses Test). Presence was selectively enhanced in VR relative to desktop play, whereas immersion was highest in the VR plus treadmill condition. Specific ASC dimensions related to embodiment and self-experience were selectively elevated in immersive conditions, with the most robust effects observed for disembodiment and positive depersonalization. Retrospective time-estimation accuracy was reduced in the highest immersion condition, indicating increased temporal distortion. Immersive gameplay did not produce widespread changes in executive function. Overall, the findings indicate that immersive virtual reality gameplay selectively alters embodiment-related aspects of conscious experience and retrospective time perception, without broadly changing executive function. Full article

Autonomous navigation in underwater environments is challenged by the absence of GPS, degraded visibility, and submerged obstacles. This article investigates these issues using the BlueROV2, an open platform for scientific experimentation. We propose a deep reinforcement learning approach based on the Proximal Policy Optimization (PPO) algorithm, using an observation space that combines target-oriented navigation information, a virtual occupancy grid, and raycasting along the boundaries of the operational area. This information is encoded into a high-dimensional observation space of 84 dimensions, providing the agent with comprehensive local and global situational awareness. The learned policy is compared against a reference deterministic kinematic planner, the Dynamic Window Approach (DWA), a robust baseline for obstacle avoidance. The evaluation is conducted in a realistic simulation environment and complemented by validation on a physical BlueROV2 supervised by a 3D digital twin of the test site, reducing risks associated with real-world experimentation. The results show that the PPO policy consistently outperforms DWA in highly cluttered environments, notably thanks to better local adaptation and reduced collisions. Finally, experiments demonstrate the transferability of the learned behavior from simulation to the real world, confirming the relevance of deep RL for autonomous navigation in underwater robotics. Full article

The integration of virtual museums into education has emerged as an innovative approach embraced by both teachers and learners, reflecting the broader impact of virtual reality (VR) applications in education. This study puts forward a pedagogical framework for utilizing virtual museums in teaching art history and investigating their impact on the art history curriculum. In this context, two free online museums are used as teaching materials, representing 3D interactive learning environments that enable immersive exploration of cultural heritage. Grounded in the Theory of Technology-Mediated Learning, this research adopts a hybrid methodological approach to track the art history courses of 75 Chinese undergraduates through experiments, questionnaires, and structured interviews over a four-week period. The findings demonstrate that virtual museum-integrated instruction significantly enhances learning effectiveness over sustained use, actively promotes learner engagement, and fosters greater autonomy. Importantly, learners prioritize educational value and authenticity in virtual museum features, while also expressing a strong preference for technologically mature platforms. This research contributes to understanding the impact of VR on digital transformation in the educational sector by providing a validated instructional model that integrates virtual museums into art history curricula, offering educators a replicable framework for implementation. Future studies should investigate the relationship between emotional engagement and academic performance within virtual museums to further refine both pedagogical strategies and educational virtual reality design. Full article

Corporate food museums are increasingly recognised as strategic heritage infrastructures capable of mediating between industrial memory, territorial identity, and contemporary societal challenges. This paper proposes a conceptual shift that repositions corporate food museums from static repositories of brand heritage to Living Labs for sustainable, inclusive, and participatory food innovation. Drawing on the EU-funded GNAM project, the study adopts a qualitative methodology combining the mapping of Italian corporate food museums with an analysis of European Living Labs in the food and agri-food domain. The comparative framework informs the development of a heritage-driven Living Lab model articulated around three interconnected dimensions: cultural heritage valorisation, community engagement, and sustainable food system innovation. The model is empirically grounded through a series of design-driven workshops, technology-transfer activities, and digital engagement initiatives conducted within corporate museums and academic laboratories in Southern Italy. These include co-creation processes involving students, citizens, companies, and researchers; experimentation with food waste valorisation, biodegradable and hybrid materials, and 3D food printing; and the deployment of digital platforms and immersive virtual environments. The paper contributes to heritage studies by advancing a replicable framework in which corporate food museums act as active agents of sustainable transformation, linking cultural heritage, technological experimentation, and community participation. Full article

A stereo vision framework is designed to improve disparity estimation in occluded and boundary regions, targeting autonomous driving scenarios. The proposed architecture combines frozen Distilled Internally Normalized Optimized Version 2 features with a modular three-stage attention fusion strategy, which consists of bottom-up semantic propagation, top-down detail enhancement, and cross-view attention mechanisms. These stages jointly enforce semantic consistency, structural integrity, and accurate correspondence modeling. The fused features are then processed by an Iterative Geometry Encoding and Volumetric regression-based disparity estimation module for multi-stage regression and iterative refinement. A three-phase training pipeline is employed, including pretraining on SceneFlow, fine-tuning on virtual Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) benchmarks, and adaptation to the KITTI and ETH Zurich 3D benchmark dataset. The model achieves an out-of-center, non-occluded pixel error of 7.45% on KITTI2012 and a D1-all error of 4.10% on KITTI2015. Beyond quantitative performance, the proposed method produces visually superior disparity maps. The enhancements of boundary sharpness, occlusion completion, and structural coherence demonstrate the strong potential of the proposed algorithm for real-world deployment in dynamic and complex environments. Full article

This study introduces a novel virtual robotic approach for automated characterization of pedestrian network accessibility from semantically segmented 3D LiDAR point clouds. With approximately 8 million Canadians living with disabilities, scalable accessibility assessment methods are critical. The proposed methodology integrates a Tangent Bug navigation algorithm—extended from 2D to 3D point cloud environments—with a triangular virtual robot grounded in ADA and IBC accessibility standards. The robot navigates classified point cloud data to simultaneously extract related parameters per step including those related to the accessibility assessment, including running slope, cross-slope, path width, surface type, and step height, aligned with the Measure of Environmental Accessibility (MEA) framework. Unlike existing approaches, the method characterizes not only formal sidewalk segments but also the critical transitional linkages between building entrances and the pedestrian network. Rather than evaluating features against fixed binary thresholds, it records continuous raw measurements enabling personalized accessibility assessment tailored to individual user profiles. Quantitative validation demonstrates high accuracy for path width (NRMSE = 2.71%) and reliable slope tracking. The proposed approach is faster, more cost-effective, and more comprehensive than traditional manual methods, and its segment-independent architecture makes it well-suited for future city-scale deployment. Full article

Virtual tours are increasingly used in education, particularly when access to real environments is limited. This study examined how display mode and representation format affect subjective user experience in an educational virtual tour of a hospital operating room. A within-subject 2 × 2 design compared two representation formats (360° photographs vs. 3D models) and two display modes (desktop PC vs. immersive virtual reality using Meta Quest 2). Eighty-four university students completed the four visualization conditions and evaluated each experience using an adapted version of the QUXiVE questionnaire. Descriptive statistics and internal consistency indices were calculated, and each questionnaire dimension was analyzed using a two-way repeated-measures ANOVA with display mode and representation format as within-subject factors. A significant main effect of display mode was found for presence, engagement, immersion, flow, emotion, judgment, physical consequences, and perceived educational usefulness (all p < 0.001), but not for usability (p = 0.273). A significant main effect of representation format was observed for presence (p = 0.003), emotion (p = 0.018), and perceived educational usefulness (p = 0.015), whereas no significant interaction effects were found. These findings indicate that immersive VR had the strongest and most consistent effect on subjective user experience across both 360° and 3D virtual tours, although it was also associated with higher physical-consequence scores. By contrast, the effect of representation format was more limited. Overall, both approaches appear to be complementary educational resources, depending on pedagogical goals, available infrastructure, and desired levels of interactivity. Full article

3D digitization of museum artifacts is essential for both their virtual presentation and re-exhibition in the event of damage or loss. Given the number of artifacts that can be exhibited in a museum, the effectiveness of single-digitization practices under designed conditions is limited in terms of realism. In this study, a highly detailed sine quadrant object was digitized in a museum environment using photogrammetry and structured-light scanning (SLS) techniques. 3D models were generated from point clouds derived in photogrammetry and directly obtained from SLS. In the qualitative assessment based on the distinguishability of linear and edge details, the photogrammetric technique was found to be better; in the quantitative assessment based on the reference length values on the artifact, SLS was better, while photogrammetry was also found to be adequate. The maximum difference values for photogrammetry and SLS were 0.40 and 0.27 cm, respectively, while the average difference values were 0.24 cm and 0.10 cm. Additionally, cloud-to-cloud distance analysis revealed that two-point clouds overlapped quite well geometrically. Point clouds were also compared in terms of homogeneity using outlier detection analysis. This analysis showed that noise in the photogrammetric point cloud had a wider distribution over the artifact. In terms of data acquisition and processing time, SLS was found to be better, while the cost was comparable. After evaluating the techniques from various perspectives, photogrammetry was found to be preferable for modeling in a museum environment due to the priority need for high texture quality from the end-user’s perspective. In this respect, SLS is highly dependent on hardware capability for both data acquisition and processing. Full article

The traditional craft of Buddha statue carving represents an important form of cultural heritage in many Asian societies, yet the transmission of this knowledge is increasingly threatened by modernization and the declining number of skilled artisans. This study explores the use of Virtual Reality (VR) as an innovative tool for preserving and teaching the cultural knowledge associated with Salah Lanna stone Buddha carving. A VR-based learning environment was developed to simulate traditional carving techniques, tools, and cultural narratives related to Lanna Buddhist art. The system was designed using Unity 3D and integrated hand-tracking interaction to enable immersive practice of carving procedures. The prototype was evaluated through expert review involving ten specialists in Buddha carving, art education, and VR technology. The evaluation assessed five dimensions: usability, authenticity, cultural relevance, immersion, and perceived learning potential. Results indicate high levels of expert evaluation results regarding the effectiveness of the system, with average scores of 4.6 for usability, 4.8 for authenticity, 4.7 for cultural relevance, 4.5 for immersion, and 4.9 for perceived learning potential on a five-point scale. The findings suggest that VR technology can provide a promising platform for preserving traditional craftsmanship and supporting immersive cultural learning. By integrating technical training with cultural narratives, the system demonstrates potential for enhancing access to traditional craft education while contributing to the digital preservation of Salah Lanna cultural heritage. Full article

Italian hand gestures constitute a distinctive and widely recognized form of nonverbal communication, deeply embedded in everyday interaction and cultural identity. Despite their prominence, these gestures are rarely formalized or systematically taught, posing challenges for foreign speakers and visitors seeking to interpret their meaning and pragmatic use. Moreover, their ephemeral and embodied nature complicates traditional preservation and transmission approaches, positioning them within the broader domain of intangible cultural heritage. This paper introduces a machine learning–based framework for recognizing iconic Italian hand gestures, designed to support cultural learning and engagement among foreign speakers and visitors. The approach combines RGB–D sensing with depth-enhanced geometric feature extraction, employing interpretable classification models trained on a purpose-built dataset. The recognition system is integrated into a non-immersive virtual reality application simulating an interactive digital totem conceived for public arrival spaces, providing tutorial content, real-time gesture recognition, and immediate feedback within a playful and accessible learning environment. Three supervised machine learning pipelines were evaluated, and Random Forest achieved the best overall performance. Its integration with an Isolation Forest module was further considered for deployment, achieving a macro-averaged accuracy and F1-score of 0.82 under a 5-fold cross-validation protocol. An experimental user study was conducted with 25 subjects to evaluate the proposed interactive system in terms of usability, user engagement, and learning effectiveness, obtaining favorable results and demonstrating its potential as a practical tool for cultural education and intercultural communication. Full article