Virtual Scene Research Articles

Does the Aubert-Fleischl phenomenon affect perceived object speed in realistic virtual scenes?

Does the Aubert-Fleischl phenomenon affect perceived object speed in realistic virtual scenes?

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

TrackAER: real-time event-based quantitative flow visualization

We present a novel event-based quantitative flow visualization system, TrackAER, capable of continuously reconstructing, rendering and recording particle tracks in large test volumes without limitations on the measurement duration. Multiple event-based cameras are synchronized and calibrated to produce independent and asynchronous, yet temporally co-registered data streams of flow tracer positions. Subsequently, these data streams are merged into time-resolved three-dimensional particle tracks using photogrammetric techniques. Due to the operating principle of event cameras, the flow scenery is reduced to moving objects only, which effectively compresses the data stream at the camera source. In combination with an efficient data processing pipeline, the measurement system operates in real-time, reconstructing and rendering the investigated flow field without noticeable time lag. The data processing approach follows a “per-event” paradigm and enables the immediate observation and analysis of both, transient and long duration flow features. Specific issues resulting from event-based, frame-free processing are discussed as well as the advantages and limitations of event cameras. Exemplary results are provided to demonstrate the utility of the TrackAER system where Lagrangian particle track information is displayed in a virtual scene together with extracted quantitative information such as local flow velocities.

The Influence of Immersive and Collaborative Virtual Environments in Improving Spatial Skills

The use of Virtual Reality (VR) is growing in popularity in educational contexts. In this work, we present a novel software application based on immersive VR in which users can interact simultaneously with a model in a shared virtual scene and maintain audiovisual communication. The 3D model-building activity within the application was designed to improve spatial skills. Twenty-nine industrial engineering students completed the modeling activity in groups of two or three. Post-test scores showed significant improvement over pre-test scores for both spatial tests used to measure the effectiveness of the instrument in improving spatial skills. Participants rated the application favorably in terms of usability and functionality. Results suggest that spatial training in a collaborative immersive virtual environment has the potential to be an effective approach to improving spatial skills.

Research and practice on the application of computer virtual technology in arts and crafts nowadays

Abstract With the gradual maturation of virtual computer technology, it is increasingly used in real life to represent its effects realistically. The connection between arts and crafts and digital technology is getting closer. To bring new opportunities to the development of arts and crafts, virtual reality technology and augmented reality technology is integrated to establish a 3D arts and crafts virtual design model with visualized interactive arts and crafts design as the core. In the visualized virtual scene with the support of a craft knowledge base, the coordinates of virtual design tools, original objects inside the tools, and other points are derived according to the pick-up points. By repeating “delete current scene - generate new scene”, the design of arts and crafts is realized. After the verification of properties, performance, and satisfaction, the process error of the virtual design model is only 1.57mm, the average design cycle is 5.91 hours, and the labor cost, pre-preparation cost, and design cost are 214.66 RMB, 100 RMB, and 20 RMB respectively, which are significantly lower than the traditional design mode. Furthermore, the average scoring value of designers for different forms of arts and crafts is greater than 8 points, which is more satisfying with the application effect of the model. The model is connected to design practice with the help of virtual technology, which not only improves work efficiency and saves material and labor consumption but also facilitates the optimization of design solutions and the extension of solution serialization.

Design and development of networked real-life cycling training system based on mixed reality technology

The intelligent riding platform cooperates with the PC-side riding training system to simulate the effect of outdoor riding with a certain sense of reality. With the rapid development of information technology, users have also put forward higher requirements for cycling training systems. The 3D virtual scene riding mode provided by the existing intelligent riding training system does not meet the user's preference for real riding, and the domestic riding platform cannot simulate the real road feeling, lacking realism and fun. Therefore, developing a richer and more interesting new cycling training mode and creating a more realistic and accurate real cycling experience based on mixed reality are the new demands of users for the cycling training system. At the same time, the calculation and development of rider power based on the platform is the core of it. In the original single-person riding training mode, the server framework and client-side functional modules are constructed by using the principle of network communication and related technologies such as the production of online game rooms to realize the multi-player competitive function.

Illumination-guided inverse rendering benchmark: Learning real objects with few cameras

The realm of 3D computer vision and graphics has experienced exponential growth recently, enabling the creation of realistic virtual environments and digital representations of real-world objects. Central to this progression are 3D reconstruction methods that facilitate the virtualization of shape, color, and surface details of real objects. Current methods predominantly employ neural scene representations, which despite their efficacy, grapple with limitations such as necessitating a high number of captured images and the complexity of transforming these representations into explicit geometric forms.An alternative strategy that has gained traction is the deployment of methods such as physically-based differentiable rendering (PBDR) and inverse rendering. These approaches require fewer viewpoints, yield explicit format results, and ensure a smoother transition to other representation methods. To meaningfully assess the performance of different 3D reconstruction methods, it is imperative to utilize benchmark scenes for comparison.Despite the existence of standard objects and scenes within the literature, there is a noticeable deficiency in real-world benchmark data that concurrently captures camera, illumination, and scene parameters — all critical to high-fidelity 3D reconstructions using PBDR and inverse rendering-based methods. In this research, we introduce a methodology for capturing real-world scenes as virtual scenes, integrating illumination parameters alongside camera and scene parameters to enhance the veracity of virtual representations. In addition, we introduce a set of ten real-world scenes, along with their virtual counterparts, designed as benchmarks. These benchmarks encompass a fundamental variety of geometric constructs, including convex, concave, plain, and mixed surfaces. Additionally, we demonstrate the 3D reconstruction results of state-of-the-art 3D reconstruction methods employing PBDR in real-world scenes, using both established methodologies and our proposed one.

The robot eyes don't have it. The presence of eyes on collaborative robots yields marginally higher user trust but lower performance

Eye gaze is a prominent feature of human social lives, but little is known on whether fitting eyes on machines makes humans trust them more. In this study we compared subjective and objective markers of human trust when collaborating with eyed and non-eyed robots of the same type. We used virtual reality scenes in which we manipulated distance and the presence of eyes on a robot's display during simple collaboration scenes. We found that while collaboration with eyed cobots resulted in slightly higher subjective trust ratings, the objective markers such as pupil size and task completion time indicated it was in fact less comfortable to collaborate with eyed robots. These findings are in line with recent suggestions that anthropomorphism may be actually a detrimental feature of collaborative robots. These findings also show the complex relationship between human objective and subjective markers of trust when collaborating with artificial agents.

PRESENTATION OF A PERSON FOR IDENTIFICATION BY A 3D APPEARANCE MODEL

The article deals with the formulation of the organizational and tactical foundations for the presentation of a person for identification by a 3D model of her appearance. The author notes that the named form of presentation for identification is a promising high-tech way of carrying out the mentioned investigative (detective) action, a logical development and a modern alternative to identification by photographs. To conduct the proposed study, a virtual (computer) scene was created, within which a demonstration of 3D models of a person’s appearance was worked out. It is advisable to involve at least two attesting witnesses in this procedural action and (or) to continuously film the identifying person at the same time as capturing the screen on which 3D models are shown. 3D models can be presented as recognizable faces on a monitor screen, in virtual reality (using VR glasses or a helmet) or in augmented reality (using AR glasses). For identification, it is proposed to present 3D models of a recognizable face and at least three extras made using the forensic (signal) 3D scanning method. It is advisable to demonstrate the mentioned models to a recognizable person together and separately, as well as in a model of the environment in which the perception took place. In order to introduce into law enforcement, practice the procedure for presenting a person for identification using a 3D model, it is necessary to make appropriate changes to parts 6 and 7 of Article 228 Code of Criminal Procedure of Ukraine.

NEnv: Neural Environment Maps for Global Illumination

AbstractEnvironment maps are commonly used to represent and compute far‐field illumination in virtual scenes. However, they are expensive to evaluate and sample from, limiting their applicability to real‐time rendering. Previous methods have focused on compression through spherical‐domain approximations, or on learning priors for natural, day‐light illumination. These hinder both accuracy and generality, and do not provide the probability information required for importance‐sampling Monte Carlo integration. We propose NEnv, a deep‐learning fully‐differentiable method, capable of compressing and learning to sample from a single environment map. NEnv is composed of two different neural networks: A normalizing flow, able to map samples from uniform distributions to the probability density of the illumination, also providing their corresponding probabilities; and an implicit neural representation which compresses the environment map into an efficient differentiable function. The computation time of environment samples with NEnv is two orders of magnitude less than with traditional methods. NEnv makes no assumptions regarding the content (i.e. natural illumination), thus achieving higher generality than previous learning‐based approaches. We share our implementation and a diverse dataset of trained neural environment maps, which can be easily integrated into existing rendering engines.

The Effects of Spherical Video-Based Virtual Reality and Conventional Video on Students’ Descriptive Writing Achievement and Motivation: A Comparative Study

This study compares the effects of Spherical Video-Based Virtual Reality (SVVR) and Conventional Video (CV) on students’ writing achievement and motivation. A quasi-experimental method was used in a primary school’s Chinese Descriptive Article Writing courses. Twenty-eight fourth-grade students were randomly divided into two groups. In the SVVR group, students observed the writing scenes using SVVR devices. In the CV group, students watched the writing scenes through conventional video. SPSS software was used for the statistical analysis of the data. The results show that: (1) there is no significant difference ( U = 714, p = .061) in the overall writing achievement between SVVR and CV groups. However, students in the SVVR group performed significantly better in Organization ( U = 693, p = .029) and Content ( U = 609, p = .003) than those in the CV group. (2) Overall writing motivation improved in both SVVR and CV groups. The improvement of writing expectations in the CV group was significantly higher than that of the SVVR group ( t = −2.119, p = .044). Therefore, we suggest that schools: (1) design the integration method of SVVR and writing learning to solve the problem of the gap between students’ immersive situational experience and completing their writing goals. (2) use the immersive and interactive features of SVVR technology to create virtual experience scenes to improve students’ observation and description abilities. (3) add activity clues to guide students to experience SVVR scenes purposefully and plan and transform situational interest into learning ability. (4) further optimize SVVR devices to improve their usability during learning.

Accuracy of Femoral Tunnel Localization With Mixed Reality Technology-Assisted Single-Bundle ACL Reconstruction.

It is clinically challenging to accurately drill femoral and tibial tunnels to reconstruct the anterior cruciate ligament (ACL). Mixed reality (MR) technology, a further development of virtual reality technology, presents virtual scene information in real time and establishes an interactive feedback information loop among the real world, the virtual world, and the user. The purpose of this study was to investigate the structural and early clinical outcomes of ACL reconstruction assisted by MR technology. It was hypothesized that MR technology would improve the accuracy of tunnel localization. Cohort study; Level of evidence, 3. Included were 44 patients at a single institution who underwent arthroscopic single-bundle ACL reconstruction between June 2020 and March 2022. Reconstruction with the aid of MR technology was performed in 21 patients (MR group), and conventional arthroscopic reconstruction was performed in 23 patients. Postoperatively, the parameters related to the bone tunnel positioning were compared by computed tomography imaging with 3-dimensional (3D) reconstruction, and 12-month postoperative clinical outcomes were assessed with the Lysholm and International Knee Documentation Committee scores. There was no statistically significant difference in projection angles in the coronal, axial, or sagittal plane between the preoperative virtually created tunnel guide pin and the actual tunnel (P > .05 for all). In the MR group, the center of the femoral tunnel exit was closer to the apex of the lateral femoral condyle along the proximal-distal axis (14.07 ± 4.12 vs 17.49 ± 6.24 mm for the conventional group; P < .05) and the graft bending angle was lower (117.71° ± 8.08° vs 127.81° ± 11.91° for the conventional group; P < .05). The scatterplot of the femoral tunnel location distribution showed that the entrance and exit points in the MR group were more concentrated and closer to the ideal location of the preoperative design than in the conventional group. Patients in both groups had significant preoperative-to-postoperative improvement based on outcome scores (P < .001 for all), with no significant difference between groups. ACL reconstruction with the aid of MR technology allowed for more accurate positioning and orientation of the femoral tunnel during surgery when compared with conventional reconstruction.

On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR.

Head tracking in head-mounted displays (HMDs) enables users to explore a 360-degree virtual scene with free head movements. However, for seated use of HMDs such as users sitting on a chair or a couch, physically turning around 360-degree is not possible. Redirection techniques decouple tracked physical motion and virtual motion, allowing users to explore virtual environments with more flexibility. In seated situations with only head movements available, the difference of stimulus might cause the detection thresholds of rotation gains to differ from that of redirected walking. Therefore we present an experiment with a two-alternative forced-choice (2AFC) design to compare the thresholds for seated and standing situations. Results indicate that users are unable to discriminate rotation gains between 0.89 and 1.28, a smaller range compared to the standing condition. We further treated head amplification as an interaction technique and found that a gain of 2.5, though not a hard threshold, was near the largest gain that users consider applicable. Overall, our work aims to better understand human perception of rotation gains in seated VR and the results provide guidance for future design choices of its applications.

Color multilayer holographic near-eye augmented reality display

This study demonstrates a full-color near-eye holographic display capable of superimposing color virtual scenes with 2D, 3D, and multiple objects with extended depth upon a real scene, which also has the ability to present different 3D information depending on the focus of the user’s eyes using a single computer-generated hologram per color channel. Our setup makes use of a hologram generation method based on two-step propagation and the singular value decomposition of the Fresnel transform impulse response function to efficiently generate the holograms of the target scene. Then, we test our proposal by implementing a holographic display that makes use of a phase-only spatial light modulator and time-division multiplexing for color reproduction. We demonstrate the superior quality and computation speed of this approach compared with other hologram generation techniques with both numerical and experimental results.

System Development and Evaluation of Human–Computer Interaction Approach for Assessing Functional Impairment for People with Mild Cognitive Impairment: A Pilot Study

Non-pharmacological treatments have gained significant attention in the field of cognitive impairment. Among them, human–computer interaction-based (HCI) methods have emerged as a promising approach due to their broad applicability and convenience in assessing symptoms associated with this progressively debilitating condition. However, existing rehabilitation training systems for cognitive impairment lack effective assessment methods to meet the diverse rehabilitation needs of users. In this article, we surveyed existing HCI-based cognitive rehabilitation training systems and analyzed their advantages and shortcomings. Drawing from the insights gained from these systems, we propose a novel Leap Motion-based building block training system that incorporates system software capable of generating highly realistic virtual scenes, with the added capability of user behavior detection using Kinect. We conducted user testing of this new system, comparing the performance of a representative cohort with mild cognitive impairment (MCI) (n = 9) to that of disease-free participants (n = 10). Additionally, we conducted ergonomic experiments to assess the system’s performance in elderly people. The experimental results revealed significant differences between the MCI cohort and the control cohort. Specifically, the MCI cohort exhibited a reduced range of motion and longer task completion times compared to the control cohort. These findings have the potential to contribute to the differentiation of cognitive levels. In conclusion, our analysis of existing cognitive rehabilitation training systems provides valuable insights for researchers working on the development of future innovative cognitive rehabilitation training systems and enriches the non-pharmacological treatment models for cognitive impairment. Furthermore, the observed relationship between behavioral data, task completion times, and cognitive levels in older adults offers useful insights for the design of HCI-based approaches for diagnosing and assessing the treatment of MCI.

Ghana’s blockchain scene on WhatsApp

Social media sites with global reach like Twitter and WhatsApp are playing a paramount role in the communication, socializing, and business practices of Ghana’s blockchain community. Specifically, WhatsApp has become the platform powering the principal instantiation of blockchain in Ghana, which is trading and investing in cryptocurrencies. Considering the country’s high internet fees and sometimes unreliable network access, WhatsApp is a particularly endearing platform to facilitate the blockchain scene due to the low internet data usage that it requires. Drawing on empirical research data from 33 semi-structured interviews with blockchain enthusiasts in Ghana, this paper analyzes the particularities of blockchain’s adoption and spread in its primarily virtual scene. Key to this examination is the consideration of the affordances and constraints of WhatsApp as the primary spatial frame driving and shaping blockchain’s adoption and use in Ghana. As coagents with the digital sphere they transact and interact on, members of the blockchain community are collectively and individually perpetuating processes of knowledge creation and communal exchanges interspersed with values of competitive innovating. Keywords: Ghana, blockchain scene, WhatsApp, cryptocurrency trading and investment

REDISCOVERING CULTURAL HERITAGE SITES BY INTERACTIVE 3D EXPLORATION: A PRACTICAL REVIEW OF OPEN-SOURCE WEBGL TOOLS

Abstract. Georeferenced reconstructions can help understand the dynamic evolution of the urban context surrounding a historical site, supporting decision-making processes in the field of urban planning. The development of web applications that allow the interaction between 2D and 3D products, as well as their exploration, can facilitate virtual inspections and foster collaboration in digitization of interventions and site evolutions over time. The article discusses how virtual scene reconstructions and visits can provide alternatives to traditional in-situ tourism promotion, through digital storytelling solutions for the exploration of sites. The article also describes how 3D storytelling technologies are currently being used for dissemination cultural heritage sites. The case study of the Castello Farnese, an old XVI century heritage site in Piacenza (Italy), test the adoption of two distinct free and open-source JavaScript WebGL, Potree and Cesium, for the rendering of photogrammetric and laser scanning georeferenced scaled products and for the integration of narrative features such as annotations, camera animations, texts, and other multimedia contents. Potentials and limitations of both tools are discussed in detail, highlighting how they can be implemented for enhancing user experience in virtual tour and exploration of 3D products. In order to guarantee replicability for other case studies, source code of the implemented application is shared on GitHub along with its documentation for contributions.

Odor exposure and recall of a virtual reality crime scene: A preliminary study

Environmental context reinstatement has a particular value for recall of information in forensic interviews. Since odors are valuable memory cues and can act as memory triggers, in our preliminary study we explored whether odor exposure can help people recall details of a crime scene. The study comprised 58 women and 15 men aged 22–35 who immersed in a carefully controlled environment closely resembling an actual crime setting, i.e., a virtual reality crime. Participants were exposed to an odor at encoding, recall, both or neither of these instances, yielding a total of 4 experimental groups that further completed a memory recall task. The crime scene content recall was tested in a free recall and a forced-response test immediately after seeing the crime scene and one month later. We found no significant effects of odor exposure on the free or the cued recall of the crime scene. The memory scores correlated neither with the self-assessed olfactory/visual sensitivity of the subjects, nor with the perceived odor pleasantness. These preliminary findings suggest that introduction of a vanilla odor while encoding and recalling a crime scene does not aid witness recall accuracy.

Station biophilia – assessing the perception of greenery on railway platforms using a digital twin

ABSTRACT The increasing density of urban spaces and buildings is undermining public health. To compensate, there is a growing trend towards biophilic design, including at one of the most frequented and highly functional sites: railway stations. Previous studies have confirmed the economic feasibility of station greenery and users’ preference for this but also their reluctance to pay for greenery compared to other services. One research gap is the lack of consideration given to the full range and dose–response correlation of urban cultural ecosystem services provided by station greenery. To fill this gap, we present the development and implementation of a method using static 2D representations of virtual reality scenes generated from a digital twin. In a between-subjects experiment (N = 352), participants were randomly assigned to assess one of three levels of greenery by means of a six-item semantic differential. Supported by statistically significant differences between groups, we could identify improvements in well-being, aesthetics, stress reduction, perception of safety, psychological noise reduction and identity. Further, women were found to prefer higher levels of greenery than men. Based on our results, we recommend applying the method to evaluate planning options and that (more) greenery be introduced at metropolitan stations as part of biophilic design.

Classification and Analysis of Human Body Movement Characteristics Associated with Acrophobia Induced by Virtual Reality Scenes of Heights

Acrophobia (fear of heights), a prevalent psychological disorder, elicits profound fear and evokes a range of adverse physiological responses in individuals when exposed to heights, which will lead to a very dangerous state for people in actual heights. In this paper, we explore the behavioral influences in terms of movements in people confronted with virtual reality scenes of extreme heights and develop an acrophobia classification model based on human movement characteristics. To this end, we used wireless miniaturized inertial navigation sensors (WMINS) network to obtain the information of limb movements in the virtual environment. Based on these data, we constructed a series of data feature processing processes, proposed a system model for the classification of acrophobia and non-acrophobia based on human motion feature analysis, and realized the classification recognition of acrophobia and non-acrophobia through the designed integrated learning model. The final accuracy of acrophobia dichotomous classification based on limb motion information reached 94.64%, which has higher accuracy and efficiency compared with other existing research models. Overall, our study demonstrates a strong correlation between people’s mental state during fear of heights and their limb movements at that time.