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The way medical students learn anatomy is constantly evolving. Nowadays, technologies such as tablets support established learning methods like drawing. In this study, the effect of drawing on a tablet on medical students' anatomy learning was investigated compared to drawing or summarizing on paper. The quality of drawings or summaries was assessed as a measure of the quality of strategy implementation. Learning outcome was measured with an anatomy test, both immediately afterward and after 4-6 weeks to assess its sustainability. There were no significant group differences in learning outcome at both measurement points. For all groups, there was a significant medium strength correlation between the quality of the drawings or summaries and the learning outcome (p < 0.05). Further analysis revealed that the quality of strategy implementation moderated outcomes in the delayed test: When poorly implemented, drawing on a tablet (M = 48.81) was associated with lower learning outcome than drawing on paper (M = 58.95); The latter (M = 58.89) was related to higher learning outcome than writing summaries (M = 45.59). In case of high-quality strategy implementation, drawing on a tablet (M = 60.98) outperformed drawing on paper (M = 52.67), which in turn was outperformed by writing summaries (M = 62.62). To conclude, drawing on a tablet serves as a viable alternative to paper-based methods for learning anatomy if students can make adequate use of this strategy. Future research needs to identify how to support student drawing, for instance, by offering scaffolds with adaptive feedback to enhance learning.

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design., Competing Interests: The author declares that he has no conflicts of interest., (Copyright © 2022 Xingping Li.)

The purpose of this paper is to examine how infographics are used in the dynamics of designing or creating in the teaching and learning process. Utilising a bibliometric approach and content analysis, we examined a sample of 760 documents from the Web of Science and Scopus databases with no lower time limit until November 2023. The analyses aimed to identify trends, including main topics, authors, sources, most cited articles, institutional affiliations, and countries in the literature. This bibliometric approach allows for the observation of trends in the educational application of promoting visual literacy through students' design or creation of infographics in formal and informal learning settings. Initial publications emerged in 2007, gaining momentum from 2014 to the present. The most prolific fields were medical science, chemistry, and computer science. This investigation shows that educational institutions could employ infographics beyond present boundaries to prepare students--particularly Generation Alpha, digital natives--for a data-driven society. Institutions should expand their use of infographics to improve visual literacy and address the changing educational requirements of students in a dynamic learning environment. Overall, this paper provides insights into the readiness of educators and institutions to engage Generation Alpha, the digital natives, through visual literacy and infographics.

[Display omitted] • This special section includes papers on computer graphics education. • These papers are the best papers from the Eurographics 2022 Education Papers Program. • Specific topics include ray-tracing, the Vulkan API, and virtual reality. [ABSTRACT FROM AUTHOR]

Ray tracing is a computer graphics technique used to produce realistic visuals by physically simulating the behavior of light. Although this technique can be described straightforwardly, fully comprehending it might be challenging. It is typically taught in the classroom using the 2-D formats, such as paper or a blackboard. We propose using immersive environments for incrementing the understanding of ray tracing. We focus on improving the knowledge of the technique in experienced users, particularly Master of Computer Science students minoring in a computer-graphics-related area. We argue that exploring the ray tracing process in an immersive visualization environment can further improve the understanding of ray tracing acquired using conventional means. With that objective, this study starts by presenting RayTracerVR, a virtual reality prototype tool for learning the mechanisms of ray tracing. This tool can be used to visually explore and interact with the different aspects of the technique. It allows users to observe the progression of the rays throughout the sequential stages of the ray tracing process and analyze its corresponding computer pseudocode. The study includes user evaluation where RayTracerVR is employed to assess improvements in ray tracing understanding. The prototype's usability is also assessed. The findings indicate that using the ray tracing immersive learning environment results in a supplemental increase in understanding in users who have previously learned ray tracing using conventional means.

This paper aims to analyse to what extent the engineering curricula contribute to the development of visual literacy among engineering students. Communicating and thinking visually is a basic skill for all engineers, being a fundamental part of their educational stage. For this purpose, Engineering Graphics subjects from 30 engineering schools from Spain have been selected to review their content. The sample consists of 80 subjects of Engineering Graphics, mechanical design and computer-aided design. The curricula were analyzed using a quantitative and qualitative method. A database has been developed where keywords that identify the objectives of the analysis have been tabulated. Learning technical visual language and visualization skills as a means of developing visual literacy in engineering has been taken as the basis of the analysis. The results show that the curricula of the subjects contemplate the development of graphic skills around communication and technical representation and, to a lesser extent, visualization. On the other hand, the teaching activities contemplated do not dive into the cognitive aspects of graphics. We can also observe the pre-eminence of computer graphics as the main basis for teaching activities and marginal use of freehand drawing, which is basic for the development of Visual Literacy.

Analogical thinking is one of the thinking processes that is associated with creativity. This paper explores how it is experienced in Graphic Design elaborations using Computer Graphic Technology (CGT). Data are collected through concurrent and retrospective probing from three undergraduates and analysed through phenomenological reduction to identify the constituents and elements of the creative analogy's general psychological structure. The findings show that the general structure of the creative analogical experience is a relationship between three constituents namely: (1) the experience of recalling knowledge or information; (2) the use of knowledge for ideation; and (3) the use of knowledge to conclude ideas. The findings also show that the underlying elements of these constituents are conceptual (declarative) and technical (procedural) knowledge and its combinations that are consistent with the theories of analogical thinking. This article concludes with recommendations for future research and insights on its implementation in Design Education.

In this special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), we are pleased to present a subset of papers from the 29th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2022), held virtually March 12-16, 2022, in Christchurch, New Zealand. [ABSTRACT FROM AUTHOR]

Digital heritage portal interfaces are generally similar to digital library and search engine interfaces in displaying search results as a list of brief metadata records. The knowledge organization and search result display of these systems are item-centric, with little support for identifying relationships between items. This paper proposes a knowledge graph system and visualization interface as a promising solution for digital heritage systems to support users in browsing related items, understanding the relationships between items, and synthesizing a narrative on an issue. The paper discusses design issues for the knowledge graph, graph database, and graph visualization, and offers recommendations based on the authors' experience in developing three knowledge graph systems for archive and digital humanities resources: the Zubir Said personal archive collection at the Nanyang Academy of Fine Arts, Singapore; Singapore Pioneers social network; and Polyglot Medicine knowledge graph of Asian traditional and herbal medicine. Lessons learned from a small user study are incorporated in the discussion. [ABSTRACT FROM AUTHOR]

Game-Based Learning (GBL) is increasingly widespread as a learning technique in the engineering studies. However, this innovative methodology may be difficult to incorporate in some subjects due to their complex contents. This paper aims at combining traditional learning methodologies with game mechanics by using an ad-hoc gaming web environment and mobile devices to analyse the academic performance and motivation in engineering studies. A comparative study was carried out with 96 first-year engineering students. In this experience, the students were randomly divided into two groups: Control Group (CG) and Experimental Group (EG). The first group made use of the traditional learning methodology used, whilst the second group combined the traditional methodology with GBL and Information and Communication Technologies (ICT). The obtained results show that EG students significantly improve their academic performance and motivation, expressing a succinctly greater interest in the studied subject. Therefore, the integration of GBL in the teaching methodology could have a very positive impact as well as it could greatly ease the teaching-learning process in engineering studies.

New and rapidly‐evolving classes of display devices bridge the gap between us and the immersive experiences of the future. The most intimate of these displays are the Virtual‐ and Augmented‐Reality (VR and AR) ones, because they are capable of presenting synthetic environments that rival those in the real world. This ecosystem of personal and highly‐immersive displays offers new challenges for research in computer graphics, display technologies, and human visual perception. While the extensive advancements in the areas of display and computer graphics technologies traditionally end at the on‐screen "image," there are several untapped opportunities for advances that exploit the interplay between the display characteristics and how our visual system perceives them. In this article, we review recent progress in understanding and modeling the perception of immersive displays, as well as perceptually optimizing display technologies for immersive experiences. We present this review in the form of a taxonomy that maps the various properties of modern displays with the perceptual phenomenon that most closely interacts with them. From this taxonomy, we deduce several unsolved challenges in understanding human perception of displays, as well as perceptually‐optimal characteristics of future displays. [ABSTRACT FROM AUTHOR]

Formal learning environments have struggled to introduce STEM content and STEM careers to students from underrepresented populations. This problem is exacerbated when dealing with students who live in rural areas where access to quality materials and instruction is limited. The eMentorship project is an innovative approach to mentoring that utilizes the latest in communication technology to help support a formal mentoring program. This study investigates the impact of a virtual mentorship program on underrepresented students living in rural North Carolina. The project was an eight-week mentoring program that matched underrepresented student teams with graduate student mentors from a local university. In addition to the mentoring experience student participants were also taught three-dimensional (3-D) modeling skills using a "flipped" classroom approach to instruction. The paper presents the curriculum developed to guide the program, student examples of 3-D models, self-efficacy scores and qualitative interviews conducted with the student participants. Results of the study provide evidence that students found great value in the mentoring experience and were able to learn 3-D modeling skills in a virtual learning environment.

The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement in the field of computer graphics and animation. This survey aims to provide a comprehensive overview of the current state and future prospects of EI-assisted animation design, focusing on the challenges and opportunities presented by large model implementations. Edge intelligence, characterized by its decentralized processing and real-time data analysis capabilities, offers a transformative approach to handling the computational and data-intensive demands of modern animation. This paper explores various aspects of EI in animation and then delves into the specifics of large models in animation, examining their evolution, current trends, and the inherent challenges in their implementation. Finally, the paper addresses the challenges and solutions in integrating EI with large models in animation, proposing future research directions. This survey serves as a valuable resource for researchers, animators, and technologists, offering insights into the potential of EI in revolutionizing animation design and opening new avenues for creative and efficient animation production. [ABSTRACT FROM AUTHOR]

However, as host-switch graph cannot represent such systems, the authors investigate interconnection networks with multi-port hosts by introducing a multi-ported host-switch graph. The past years have seen a flurry of activity in the area of distributed computing and its related fields. The accepted papers for this special issue address the following aspects in computing and networking: Parallel and distributed computing Security and privacy Interconnection networks Artificial intelligence Self-stabilizing algorithms Parallel and distributed computing The first contribution, entitled "Efficient Parallel Implementations to Compute the Diameter of a Graph" by Takafuji et al., presents an efficient implementations of the Blocked Floyd-Warshall algorithm, which uses no barrier synchronization and invokes only one kernel call by using Single Kernel Soft Synchronization (SKSS) techniques. [Extracted from the article]

Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper's contributions. On the one hand we argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community. [ABSTRACT FROM AUTHOR]

In this special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), we are pleased to present the journal papers from the 21st IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2022), which will be held as a hybrid conference between October 17 and 21, 2022 in Singapore. ISMAR continues the over twenty year long tradition of IWAR, ISMR, and ISAR, and is the premier conference for Mixed and Augmented Reality in the world. [ABSTRACT FROM AUTHOR]

This awarded paper is: Simulation of collective pursuit-evasion behavior with runtime situational awareness by Zhenjing Yu, Tan Junyin, and Sheng Li, from Peking University, China. All papers have been reviewed by two or three reviewers of the CGI 2022 Program Committee, revised according to the reviewers' comments, and checked and reviewed again by the CAVW Editorial Board. This special issue is dedicated to the best papers of the second Call of the CGI 2022 conference. [Extracted from the article]

This paper investigates the relationship between colorization and pseudo-colorization techniques for converting grayscale images to color. Colorization strives to create visually believable color versions of monochrome images, either replicating the original colors or generating realistic, alternative color schemes. In contrast, pseudo-colorization maps grayscale intensities to pre-defined color palettes to improve visual appeal, enhance content understanding, or aid visual analysis. While colorization is an ill-posed problem with infinitely many RGB solutions, pseudo-colorization relies on mapping functions to deterministically assign colors. This work bridges these techniques by exploring the two following operations: first - deriving pseudo-color from colorized images - this allows for creating stylized or abstract representations from existing colorizations, and second - enriching color diversity in pseudo-colored images - this enhances visual appeal and attractiveness of pseudo-colored images. The paper emphasizes the centrality of decolorization (rgb-to-gray) models in both processes. It focuses on the theoretical underpinnings of these problems but complements them with illustrative examples for clarity. [ABSTRACT FROM AUTHOR]

Design and artwork driven by image sampling processing has a half-century tradition in contemporary art and computer graphics. In the past two decades, a similar approach has been used for the fabrication of abstract surface patterns for building facades. Recent advances in digital manufacturing based on industrial robots have reignited the interest toward developing new design-to-fabrication techniques which can possess intriguing visual and tectonic properties of the facades, based on image sampling processing and abstract image representation. The aim of the paper is to investigate the different strategies for creating surface patterns, based on image sampling and applying industrial robots as fabrication tools. In this paper, three different robotic fabrication strategies for generating surface patterns driven by image sampling are presented. [ABSTRACT FROM AUTHOR]

A user's personal experiences and characteristics may impact the strength of an embodiment illusion and affect resulting behavioral changes in unknown ways. This paper presents a novel re-analysis of two fully-immersive embodiment user-studies (n = 189 and n = 99) using structural equation modeling, to test the effects of personal characteristics on subjective embodiment. Results demonstrate that individual characteristics (gender, participation in science, technology, engineering or math - Experiment 1, age, video gaming experience - Experiment 2) predicted differing self-reported experiences of embodiment Results also indicate that increased self-reported embodiment predicts environmental response, in this case faster and more accurate responses within the virtual environment. Importantly, head-tracking data is shown to be an effective objective measure for predicting embodiment, without requiring researchers to utilize additional equipment.

Virtual reality (VR) research has provided overviews of locomotion techniques, how they work, their strengths and overall user experience. Considerable research has investigated new methodologies, particularly machine learning to develop redirection algorithms. To best support the development of redirection algorithms through machine learning, we must understand how best to replicate human navigation and behaviour in VR, which can be supported by the accumulation of results produced through live-user experiments. However, it can be difficult to identify, select and compare relevant research without a pre-existing framework in an ever-growing research field. Therefore, this work aimed to facilitate the ongoing structuring and comparison of the VR-based natural walking literature by providing a standardised framework for researchers to utilise. We applied thematic analysis to study methodology descriptions from 140 VR-based papers that contained live-user experiments. From this analysis, we developed the LoCoMoTe framework with three themes: navigational decisions, technique implementation, and modalities. The LoCoMoTe framework provides a standardised approach to structuring and comparing experimental conditions. The framework should be continually updated to categorise and systematise knowledge and aid in identifying research gaps and discussions.

The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational complexity and reduced stability, hindering their use in computer graphics applications that require real-time performance. With the recent explosion of data capture (mocap, video) machine learning (ML) has started to become popular as it is able to create surrogate models harnessing the huge amount of data stemming from various sources, minimizing computational time (instead of resource usage), and most importantly, approximate real-time solutions. The main purpose of this paper is to provide a review and classification of the most recent works regarding motion prediction, motion synthesis as well as musculoskeletal dynamics estimation problems using ML techniques, in order to offer sufficient insight into the state-of-the-art and draw new research directions. While the study of motion may appear distinct to musculoskeletal dynamics, these application domains provide jointly the link for more natural computer graphics character animation, since ML-based musculoskeletal dynamics estimation enables modeling of more long-term, temporally evolving, ergonomic effects, while offering automated and fast solutions. Overall, our review offers an in-depth presentation and classification of ML applications in human motion analysis, unlike previous survey articles focusing on specific aspects of motion prediction.

During mid-air interactions, common approaches (such as the god-object method) typically rely on visually constraining the user's avatar to avoid visual interpenetrations with the virtual environment in the absence of kinesthetic feedback. This paper explores two methods which influence how the position mismatch (positional offset) between users' real and virtual hands is recovered when releasing the contact with virtual objects. The first method (sticky) constrains the user's virtual hand until the mismatch is recovered, while the second method (unsticky) employs an adaptive offset recovery method. In the first study, we explored the effect of positional offset and of motion alteration on users' behavioral adjustments and users' perception. In a second study, we evaluated variations in the sense of embodiment and the preference between the two control laws. Overall, both methods presented similar results in terms of performance and accuracy, yet, positional offsets strongly impacted motion profiles and users' performance. Both methods also resulted in comparable levels of embodiment. Finally, participants usually expressed strong preferences toward one of the two methods, but these choices were individual-specific and did not appear to be correlated solely with characteristics external to the individuals. Taken together, these results highlight the relevance of exploring the customization of motion control algorithms for avatars.

In this paper, we aim to address the challenge of novel view rendering of human performers that wear clothes with complex texture patterns using a sparse set of camera views. Although some recent works have achieved remarkable rendering quality on humans with relatively uniform textures using sparse views, the rendering quality remains limited when dealing with complex texture patterns as they are unable to recover the high-frequency geometry details that are observed in the input views. To this end, we propose HDhuman, which uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network with geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering. The designed pixel-aligned spatial transformer calculates the correlations between the input views and generates human reconstruction results with high-frequency details. Based on the surface reconstruction results, the geometry-guided pixel-wise visibility reasoning provides guidance for multi-view feature integration, enabling the rendering network to render high-quality images at 2k resolution on novel views. Unlike previous neural rendering works that always need to train or fine-tune an independent network for a different scene, our method is a general framework that is able to generalize to novel subjects. Experiments show that our approach outperforms all the prior generic or specific methods on both synthetic data and real-world data. Source code and test data will be made publicly available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/HDhuman/index.html.

Haptic exoskeleton gloves are a widespread solution for providing force-feedback in Virtual Reality (VR), especially for 3D object manipulations. However, they are still lacking an important feature regarding in-hand haptic sensations: the palmar contact. In this paper, we present PalmEx, a novel approach which incorporates palmar force-feedback into exoskeleton gloves to improve the overall grasping sensations and manual haptic interactions in VR. PalmEx's concept is demonstrated through a self-contained hardware system augmenting a hand exoskeleton with an encountered palmar contact interface - physically encountering the users' palm. We build upon current taxonomies to elicit PalmEx's capabilities for both the exploration and manipulation of virtual objects. We first conduct a technical evaluation optimising the delay between the virtual interactions and their physical counterparts. We then empirically evaluate PalmEx's proposed design space in a user study (n=12) to assess the potential of a palmar contact for augmenting an exoskeleton. Results show that PalmEx offers the best rendering capabilities to perform believable grasps in VR. PalmEx highlights the importance of the palmar stimulation, and provides a low-cost solution to augment existing high-end consumer hand exoskeletons.

The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.

Presenting a predictive model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model - its risks, strengths, and limitations - making it difficult for subject matter experts to feel confident in their decision to use a model. As a result, models may fail in unexpected ways or go entirely unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we describe an iterative study conducted with both subject matter experts and data scientists to understand the gaps in communication between these two groups. We find that, while the two groups share common goals of understanding the data and predictions of the model, friction can stem from unfamiliar terms, metrics, and visualizations - limiting the transfer of knowledge to SMEs and discouraging clarifying questions being asked during presentations. Based on our findings, we derive a set of communication guidelines that use visualization as a common medium for communicating the strengths and weaknesses of a model. We provide a demonstration of our guidelines in a regression modeling scenario and elicit feedback on their use from subject matter experts. From our demonstration, subject matter experts were more comfortable discussing a model's performance, more aware of the trade-offs for the presented model, and better equipped to assess the model's risks - ultimately informing and contextualizing the model's use beyond text and numbers.

In this special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), we are pleased to present the journal papers from the 20th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2021), which will be held as a virtual conference between October 4 and 8, 2021. ISMAR continues the over twenty year long tradition of IWAR, ISMR, and ISAR, and is the premier conference for Mixed and Augmented Reality in the world. [ABSTRACT FROM AUTHOR]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics, and others. Due to its application in control theory, matrix equation A X B = C has been extensively studied. However, there is currently limited information on matrix equation A X B = C regarding the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of dual quaternion matrix equation A X B = C , and present the expression for the general solution when it is solvable. As an application, we derive the ϕ -Hermitian solutions for dual quaternion matrix equation A X A ϕ = C , where the ϕ -Hermitian extends the concepts of Hermiticity and η -Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper. [ABSTRACT FROM AUTHOR]

Rendering realistic virtual scenes from images has been a long-standing research goal in the fields of computer graphics and computer vision. NeRF (neural radiance fields) is an emerging method based on deep neural networks, which achieves realistic rendering by learning the radiance field of each point in the scene. By using neural radiance fields, not only realistic images but also realistic three-dimensional scenes can be generated, making it have a wide range of application prospects such as virtual reality, augmented reality and computer games. However, its basic model has shortcomings such as low training efficiency, poor generalization ability, insufficient interpretability, susceptible to lighting and material changes, inability to handle dynamic scenes, and other deficiencies that may result in suboptimal rendering results in certain situations. With the continuous popularity of this field, a large amount of research has been carried out, yielding impressive results in terms of efficiency and accuracy. In order to track the latest research in this field, this paper provides a review and summary of the key algorithms in recent years. This paper first outlines the background and principles of neural radiance fields, and briefly introduces the evaluation metrics and public datasets in this field. Then, a classification discussion is conducted on the key improvements to the model, mainly including: the optimization of basic NeRF model parameters, the improvement in rendering speed and inference ability, the enhancement of spatial representation and lighting ability, the improvement in camera pose and sparse view synthesis methods for static scene, and the development in dynamic scene modeling field. Subsequently, the speed and performance of various models are classified, compared and analyzed, and the main model evaluation indicators and open datasets in this field are briefly introduced. Finally, the future development trend of neural radiance field is prospected. [ABSTRACT FROM AUTHOR]

The target detection algorithm has a high accuracy rate for the detection of general objects. However, for specific kinds of objects, especially those with complex application scenarios and high requirements on recognition rates, the accuracy and real-time performance are still far behind the actual application requirements. Meanwhile, the high real-time performance of most current target detection algorithm depends on high-performance computers or Graphics Processing Units (GPUs), which limits their application in low-performance embedded system. In this paper, an embedded vehicle detection algorithm was proposed. Compared to the existing general detection algorithms, the network structure of the proposed algorithm has been improved in many aspects. Firstly, the overall network structure of YOLOv3-tiny has been improved, and the way of feature extraction has been modified to increase the network in terms of detection speed and recognition accuracy. Secondly, a spatial pyramid pooling structure is introduced into the network to enhance the learning capability, and the spatial pyramid pooling structure is also improved by increasing the number of pooling layers in the structure and cascading multiple groups of pooling layers of different sizes. Thirdly, to speed up target detection process, the original clustering algorithm is replaced by Kmean++ clustering algorithm, and the original IoU (The Intersection over Union) loss function is replaced by GIoU (Generalized IoU). Fourthly, according to the scale factor of batch normalization layer, the model is compressed and trimmed to remove redundant channels, and a small model more suitable for embedded system application is obtained. Finally, Raspberry Pi 4B and Inter NCS2 neural computing stick are used as the embedded system with the input image size of 416 × 416. Experimental tests on UA-DETRAC dataset obtain higher mAP than other advanced existing methods, indicating that the network model proposed in this paper could effectively improve the real time and accuracy of multi-vehicle target detection. [ABSTRACT FROM AUTHOR]

Background and Objective: Interactive soft tissue dissection has been a fundamental procedure in virtual surgery systems. Existing cutting algorithms involve complex topology changes of simulation meshes, which can increase simulation overhead and produce visual artifacts. In this paper, we proposed a novel graph-based shape-matching method that allows for real-time, flexible, progressive, and discontinuous cuts on soft tissue., Methods: We employed shape-matching constraints within the position-based dynamics (PBD) framework, a widely adopted approach for real-time simulation applications. The soft tissue was effectively modeled using overlapping clusters, each governed by shape-matching constraints. The dissection process was bifurcated into two distinct stages. In the first stage, the surgical scalpel presses the surface of the soft tissue. The soft tissue is cut apart when the surface pressure exceeds a threshold, entering the second stage. To address the discrepancy between the visual mesh and the simulation model during cluster separation, we developed an Aggregate Finding Connected Components (AFCC) algorithm, optimized for GPU computation and integrated with a background grid. This approach also avoids ghost forces and fragmentation artifacts. To control the increase in the number of clusters, we also propose a merging strategy that can run in parallel., Results: Our simulation outcomes demonstrated that the AFCC dissection algorithm effectively manages cluster separation and expansion with robustness. There were no ghost forces between the cutting surface and unrealistic fragments. Our simulation capability extended to supporting intricate and discontinuous cutting routes. Our dissection simulation maintained real-time performance even with over 100,000 particles constituting the soft tissue., Conclusions: Our real-time and robust surgical dissection simulation technique enables the performance of complex cuts in various surgical scenarios, demonstrating its potential in virtual surgery applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influ- ence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Random noise-moving images (noises) can make glaucoma patients with no subjective symptoms aware of visual field abnormalities. To explore this concept, we developed a noise using computer graphics (CG) and investigated the difference in the subjective perception of visual field abnormalities between CG and conventional analog noises., Methods: We enrolled individuals with glaucoma (205 eyes), preperimetric glaucoma (PPG; 19 eyes), and normal eyes (35 eyes). For a CG noise, a series of still images was made by randomly selecting five monochromatic tones on 2-mm square dots, and these images were drawn at 60 frames per second (fps) to create a noise-moving image. The participants were asked to describe their perceived shadows on a paper. The results were categorized as follows based on the pattern deviation probability map of the Humphrey field analyzer (HFA): "agreement," "partial agreement," "disagreement," and "no response." The glaucoma stage was classified into four stages, from M1 to M4, based on the HFA's mean deviation., Result: The detection rates (agreement and partial agreement) were 80.5% and 65.4% for the CG and analog noises, respectively, with CG noise showing a significantly higher detection rate in all glaucoma eyes (P < 0.001). The detection rates tended to increase as the glaucoma stage progressed, and in Stage M3, these were 93.9% and 78.8% for the CG and analog noises, respectively. The PPG eyes did not exhibit subjective abnormalities for both noises. The specificity values were 97.1% and 100% for the CG and analog noises, respectively., Conclusion: The CG noise is more effective than the analog noise in evaluating the subjective perception of visual field abnormalities in patients with glaucoma., Competing Interests: Arata Inoue has a patent for a method and device for diagnosing visual field abnormalities (patent No. JP7339900), as mentioned in this article. There are no other relevant declarations relating to employment, consultancy, further patents, products in development or marketed products. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Eiko Koike: none. Naoyuki Maeda: Paid consultancy: BVI Medical, CooperVision, Tomey Corporation. Research Grant: Senjyu Pharmaceutical Co., Ltd., Topcon Corporation. Honoraria for speaking: Alcon, Inc., Bausch + Lomb, HOYA Corporation, Johnson & Johnson, Menicon Co., Ltd., Nikon Corporation, Novartis International AG, Otsuka Pharmaceutical Co., Ltd., Santen Pharmaceutical Co., Ltd., Topcon Corporation. Chota Matsumoto: Paid consultancy: CREWT Medical Systems, Inc., Topcon Corporation. Honoraria for speaking: CREWT Medical Systems, Inc., Topcon Corporation. There are no patents, products in development or marketed products to declare. These do not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Inoue et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We present a systematic review of 111 papers that measure the impact of virtual experiences created through head-mounted displays (HMDs) on empathy. Our goal was to analyze the conditions and the extent to which virtual reality (VR) enhances empathy. To achieve this, we categorized the relevant literature according to measurement methods, correlated human factors, viewing experiences, topics, and participants. Meta-analysis was performed based on categorized themes, and under specified conditions, we found that VR can improve empathy. Emotional empathy increased temporarily after the VR experience and returned to its original level over time, whereas cognitive empathy remained enhanced. Furthermore, while VR did not surpass 2D video in improving emotional empathy, it did enhance cognitive empathy, which is associated with embodiment. Our results are consistent with existing research suggesting differentiation between cognitive empathy (influenced by environmental factors and learnable) and emotional empathy (highly heritable and less variable). Interactivity, target of empathy, and point of view were not found to significantly affect empathy, but participants' age and nationality were found to influence empathy levels. It can be concluded that VR enhances cognitive empathy by immersing individuals in the perspective of others and that storytelling and personal characteristics are more important than the composition of the VR scene. Our findings provide guiding information for creating empathy content in VR and designing experiments to measure empathy.

Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.

For VR interaction, the home environment with complicated spatial setup and dynamics may hinder the VR user experience. In particular, pets' movement may be more unpredictable. In this paper, we investigate the integration of real-world pet activities into immersive VR interaction. Our pilot study showed that the active pet movements, especially dogs, could negatively impact users' performance and experience in immersive VR. We proposed three different types of pet integration, namely semitransparent real-world portal, non-interactive object in VR, and interactive object in VR. We conducted the user study with 16 pet owners and their pets. The results showed that compared to the baseline condition without any pet-integration technique, the approach of integrating the pet as interactive objects in VR yielded significantly higher participant ratings in perceived realism, joy, multisensory engagement, and connection with their pets in VR.

In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardio-gram(EGG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary objective was to identify which of these factors plays a central role in causing discomfort when experiencing rotations along three different axes: Roll, Pitch, and Yaw. This study involved 35 participants who were tasked with destroying asteroids using their eye gaze while undergoing passive rotations in four separate sessions. The results, when combined with subjective measurements (specifically, Fast motion sickness questionnaire (FMS) and Simulator sickness questionnaire (SSQ) score), demonstrated that EGG measurements were superior in detecting symptoms associated with nausea. As for ECG measurements, our observations did reveal significant changes in Heart Rate Variability (HRV) parameters. However, we caution against relying solely on ECG as a dependable indicator for assessing the extent of cybersickness. Most notably, EEG signals emerged as a crucial resource for discerning individual differences related to these rotational axes. Our findings were significant not only in the context of periodic activities but also underscored the potential of aperiodic activities in detecting the severity of cybersickness and an individual's susceptibility to rotational triggers.

Edge-aware image smoothing refers to the removal of details with edges preserved. It is an essential topic in the field of image processing and computer graphics. In this paper, in order to achieve better edge preservation than the existing models, we propose a robust edge-preserving image filtering method based on a complementary weighting scheme. Both isotropic and anisotropic weights are involved in our model to adapt the fidelity and the regularization terms. To efficiently solve the proposed model, we introduce an effective algorithm based on additive half quadratic minimization, alternating direction of multipliers, and Fourier domain optimization strategies. We experimentally validate the proposed filter on several low-level vision tasks. Both quantitative and qualitative experimental results show significant superiority of our proposed filter compared to existing techniques. Furthermore, the filter exhibits high efficiency and is able to process 720P color images (over 10 fps) in real-time on an NVIDIA RTX 3070. Therefore, it is practical for real-world applications. [ABSTRACT FROM AUTHOR]

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system's real-time performance and universality, highlighting its user-centric design. [ABSTRACT FROM AUTHOR]

Point cloud registration is a fundamental task in computer vision and graphics, which is widely used in 3D reconstruction, object tracking, and atlas reconstruction. Learning-based optimization and deep learning methods have been widely developed in pairwise registration due to their own distinctive advantages. Deep learning methods offer greater flexibility and enable registering unseen point clouds that are not trained. Learning-based optimization methods exhibit enhanced robustness and stability when handling registration under various perturbations, such as noise, outliers, and occlusions. To leverage the strengths of both approaches to achieve a less time-consuming, robust, and stable registration for multiple instances, we propose a novel computational framework called SGRTmreg for multiple pairwise registrations in this paper. The SGRTmreg framework utilizes three components—a Searching scheme, a learning-based optimization method called Graph-based Reweighted discriminative optimization (GRDO), and a Transfer module to achieve multi-instance point cloud registration.Given a collection of instances to be matched, a template as a target point cloud, and an instance as a source point cloud, the searching scheme selects one point cloud from the collection that closely resembles the source. GRDO then learns a sequence of regressors by aligning the source to the target, while the transfer module stores and applies the learned regressors to align the selected point cloud to the target and estimate the transformation of the selected point cloud. In short, SGRTmreg harnesses a shared sequence of regressors to register multiple point clouds to a target point cloud. We conduct extensive registration experiments on various datasets to evaluate the proposed framework. The experimental results demonstrate that SGRTmreg achieves multiple pairwise registrations with higher accuracy, robustness, and stability than the state-of-the-art deep learning and traditional registration methods. [ABSTRACT FROM AUTHOR]

In recent years, most video segmentation methods use deep CNN to process the input image, but they did not fully mine the rich intermediate predictions in spatio‐temporal space. And, the segmentation challenges such as occlusion, severe deformation and illumination have not been well solved so far. To alleviate these problems, this paper focuses on constructing multi module network structures that represent multi semantics and proposes a video object segmentation network via coupled‐stream architecture with feature memory mechanism. This network first extracts high‐level semantic features, edge features, long‐term and short‐term stable depth features of the target, and then decode them into the segmentation mask of target. In addition, negative skeleton inhibition and frame interpolation are used to prevent the interference of similar objects and motion blur, respectively. The method has a low GPU memory usage, regardless of the number of object in video. And performs 86.5%and 62.4% in J&F measure on DAVIS 2016 and DAVIS 2017 validation set, without fine‐tuning and online training. [ABSTRACT FROM AUTHOR]

3D Clipping is one of the important problem in computer graphics (3D visualization). 3D clipping generally of cuboid shape but it is not compulsory. it may be in any 3D shape. It is clear that if the shape of clip window is complex then clipping would also be complicated. Lines which we want to clip would be in 3D space: which makes clipping more complex and difficult. This paper suggests a clipping method to clip the line with respect to a specific clip window. Where boundaries of clip window are the coordinate plane and an intercepting plane. This proposed algorithm use parametric equation line and compute point of intersections of line with boundaries of clip window and protect those point which lies in or on clip window. The proposed algorithm in this paper is less complex simple algorithm. [ABSTRACT FROM AUTHOR]

The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface. [ABSTRACT FROM AUTHOR]