Search

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

The subject matter of surfaces in computer aided geometric design (CAGD) is the depiction and design of surfaces in the computer graphics arena. Due to their geometric features, modeling of Bézier curves and surfaces with their shape parameters is the most well-liked topic of research in CAGD/computer-aided manufacturing (CAM). The primary challenges in industries such as automotive, shipbuilding, and aerospace are the design of complex surfaces. In order to address this issue, the continuity constraints between surfaces are utilized to generate complex surfaces. The parametric and geometric continuities are the two metrics commonly used for establishing connections among surfaces. This paper proposes continuity constraints between two generalized Bézier-like surfaces (gBS) with different shape parameters to address the issue of modeling and designing surfaces. Initially, the generalized form of C3 and G3 of generalized Bézier-like curves (gBC) are developed. To check the validity of these constraints, some numerical examples are also analyzed with graphical representations. Furthermore, for a continuous connection among these gBS, the necessary and sufficient G1 and G2 continuity constraints are also developed. It is shown through the use of several geometric designs of gBS that the recommended basis can resolve the shape and position adjustment problems associated with Bézier surfaces more effectively than any other basis. As a result, the proposed scheme not only incorporates all of the geometric features of curve design schemes but also improves upon their faults, which are typically encountered in engineering. Mainly, by changing the values of shape parameters, we can alter the shape of the curve by our choice which is not present in the standard Bézier model. This is the main drawback of traditional Bézier model. [ABSTRACT FROM AUTHOR]

The paper deals with the problematics of representation of the geometry of three-dimensional scenes in computer graphics using domain-specific hierarchical data structures based on octant trees or directed acyclic graphs as the means of space saving binary representation of this information. Several hierarchical data structures were introduced in this field in past decades. In this paper we are moving this problematic to the field of the representation of three-dimensional scenes which are rather dynamic in time, so their geometry is changing on the regular basis. We are investigating the influence of this time-variability on the size of the related hierarchical data structure on binary level. We are comparing the approach in which each time step is considered as the stand-alone static three-dimensional scene and therefore is represented by its own hierarchical data structure with the approach when this time-variable scene is considered as the four dimensional space and spatio-temporal sparse voxel octree hierarchical data structure is used for the representation of the geometry of this scene. We tested both approaches on three different scenes which were obtained by voxelization into two different resolutions of polygonal surface models represented originally as the mesh of triangles in Wavefront Object file format. Results of the tests show that spatio-temporal sparse voxel octree is better solution, when we obtained from 1.73 to 2.08 more compact binary representation, in comparison to the solution when time frames were encoded separately as the set of sparse voxel octrees. [ABSTRACT FROM AUTHOR]

Split quaternions have various applications in mathematics, computer graphics, robotics, physics, and so on. In this paper, two useful, real representations of a split quaternion matrix are proposed. Based on this, we derive their fundamental properties. Then, via the real representation method, we obtain the necessary and sufficient conditions for the existence of solutions to two split quaternion matrix equations. In addition, two experimental examples are provided to show their feasibility. [ABSTRACT FROM AUTHOR]

In this paper, we examine q-Bernstein-Bézier surfaces in Minkowski space- R13 with q as the shape parameter. These surfaces, a generalization of Bézier surfaces, have applications in mathematics, computer-aided geometric design, and computer graphics for the surface formation and modeling. We analyze the timelike and spacelike cases of q-Bernstein-Bézier surfaces using known boundary control points. The mean curvature and Gaussian curvature of these q-Bernstein-Bézier surfaces are computed by finding the respective fundamental coefficients. We also investigate the shape operator dependency for timelike and spacelike q-Bernstein-Bézier surfaces in Minkowski space- R13 , and provide biquadratic and bicubic q-Bernstein-Bézier surfaces as illustrative examples for different values of the shape controlling parameter q. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The time fractional Schrödinger equation contributes to our understanding of complex quantum systems, anomalous diffusion processes, and the application of fractional calculus in physics and cubic B-spline is a versatile tool in numerical analysis and computer graphics. This paper introduces a numerical method for solving the time fractional Schrödinger equation using B-spline functions and the Atangana-Baleanu fractional derivative. The proposed method employs a finite difference scheme to discretize the fractional derivative in time, while a θ-weighted scheme is used to discretize the space directions. The efficiency of the method is demonstrated through numerical results, and error norms are examined at various values of the non-integer parameter, temporal directions, and spatial directions. [ABSTRACT FROM AUTHOR]

In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained. [ABSTRACT FROM AUTHOR]

Most existing multi‐view stereo (MVS) methods fail to consider global context information in the stage of feature extraction and cost aggregation. As transformers have shown remarkable performance on various vision tasks due to their ability to perceive global contextual information, this paper proposes a transformer‐based feature enhancement network (TF‐MVSNet) to facilitate feature representation learning by combining local features (both 2D and 3D) with long‐range contextual information. To reduce memory consumption of feature matching, the cross‐attention mechanism is leveraged to efficiently construct 3D cost volumes under the epipolar constraint. Additionally, a colour‐guided network is designed to refine depth maps at a coarse stage, hence reducing incorrect depth predictions at a fine stage. Extensive experiments were performed on the DTU dataset and Tanks and Temples (T&T) benchmark and results are reported. [ABSTRACT FROM AUTHOR]

Recognizing the detailed information embedded in rasterized floorplans is at the research forefront in the community of computer graphics and vision. With the advent of deep neural networks, automatic floorplan recognition has made tremendous breakthroughs. However, co‐recognizing both the structures and semantics of floorplans through one neural network remains a significant challenge. In this paper, we introduce a novel framework Raster‐to‐Graph, which automatically achieves structural and semantic recognition of floorplans. We represent vectorized floorplans as structural graphs embedded with floorplan semantics, thus transforming the floorplan recognition task into a structural graph prediction problem. We design an autoregressive prediction framework using the neural network architecture of the visual attention Transformer, iteratively predicting the wall junctions and wall segments of floorplans in the order of graph traversal. Additionally, we propose a large‐scale floorplan dataset containing over 10,000 real‐world residential floorplans. Our autoregressive framework can automatically recognize the structures and semantics of floorplans. Extensive experiments demonstrate the effectiveness of our framework, showing significant improvements on all metrics. Qualitative and quantitative evaluations indicate that our framework outperforms existing state‐of‐the‐art methods. Code and dataset for this paper are available at: https://github.com/HSZVIS/Raster-to-Graph. [ABSTRACT FROM AUTHOR]

This is the first of two papers summarizing studies reporting on the design of electronic graphic symbol-based augmentative and alternative communication (AAC) systems, to determine the state of the field. The aim of this paper was to provide an overview of the general characteristics of the studies and to describe the features of the systems designed. A scoping review was conducted. A multifaceted search resulted in the identification of 28 studies meeting the selection criteria. Data were extracted relating to four areas of interest, namely (1) the general characteristics of the studies, (2) features of the systems designed, (3) availability of the systems to the public, and (4) the design processes followed. In this paper, findings relating to the first three areas are presented. Most study authors were affiliated to fields of engineering and/or computer science and came from high-income countries. Most studies reported the design of AAC applications loaded onto mobile technology devices. Common system features included customizable vocabulary items, the inclusion of graphic symbols from both established AAC libraries and other sources, a dynamic grid display, and the inclusion of digital and/or synthetic speech output. Few systems were available to the public. Limited justifications for many of the complex design decisions were provided in the studies, possibly due to limited involvement of rehabilitation professionals during the design process. Furthermore, few studies reported on the design of graphic symbol-based AAC systems specifically for middle- and low-income contexts and also for multilingual populations. Complex design decisions about electronic graphic symbol-based augmentative and alternative communication (AAC) systems should be made purposefully and with sufficient justification. Increased collaboration between designers and rehabilitation professionals during the design of electronic graphic symbol-based systems could improve the products. Design of AAC systems for populations residing in low and middle-income contexts and also for multilingual populations are urgently needed. [ABSTRACT FROM AUTHOR]

During comprehensive regional prospecting evaluation and delineation of a prospecting target area, various types of data, including geological, geophysical, geochemical, and remote sensing, are usually integrated and visualized in a unified spatial environment, making it convenient for researchers to identify mineralization. To maximize the precision of spatial boundaries, the maps traditionally used in prospecting are predominantly in vector formats. However, with the rapid development of modern real-time 3D graphics and computer cartography technology, raster maps can now provide richer detail representation compared to traditional vector maps while still meeting the precision requirements. In this paper, we present a new GPU-based 3D visualization method for spatial data, specifically, two types of bitmap-based maps called dynamic geochemical maps (DGMs) and interactive geological maps (IGMs). A novel software system implementing this method was developed and has been applied in the exploration of the Zhunuo ore district, Tibet, showing large advantages over traditional vector maps. [ABSTRACT FROM AUTHOR]

Recently, there has been renewed interest in signed distance bound representations due to their unique properties for 3D shape modelling. This is especially the case for deep learning-based bounds. However, it is beneficial to work with polygons in most computer graphics applications. Thus, in this paper, we introduce and investigate an asymptotically fast method for transforming signed distance bounds into polygon meshes. This is achieved by combining the principles of sphere tracing (or ray marching) with traditional polygonization techniques, such as marching cubes. We provide theoretical and experimental evidence that this approach is of the O (N 2 log N) computational complexity for a polygonization grid with N 3 cells. The algorithm is tested on both a set of primitive shapes and signed distance bounds generated from point clouds by machine learning (and represented as neural networks). Given its speed, implementation simplicity, and portability, we argue that it could prove useful during the modelling stage as well as in shape compression for storage. [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]

Due to the evolution of graphics processors over recent decades it has now become possible to produce high-quality, realistic three-dimensional scenes. However, aliasing occurs during the sampling process performed during rasterization, which causes a serrated effect around the edges of the objects presented in the scene, highlighting an unreal aspect of the image and causing displeasure to the viewer. This paper aims to develop an anti-aliasing treatment based on rotated spatial filtering. It undertakes edge detection by applying spatial filtering with a simple linear regression technique. A smoothing spatial filter is then rotated to match the direction of the inspected edge and is applied to the affected regions. Testing was performed on an OpenGL application, processing the rendered image from the framebuffer, and Blender, a 3D modeling software that enables scenes with more complex graphics. The results have demonstrated the effectiveness of the proposed method in smoothing aliasing with good quality while preserving the details of the scene. Hence, the problem was managed effectively with a post-filtering approach and without oversampling. The running time of the algorithm is O(n), and the memory used is O(n). [ABSTRACT FROM AUTHOR]

Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage. [ABSTRACT FROM AUTHOR]

Summary: Cracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification. [ABSTRACT FROM AUTHOR]

This study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination. [ABSTRACT FROM AUTHOR]

Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: (https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction) [ABSTRACT FROM AUTHOR]

Knitted fabric simulation seeks to create lifelike virtual representations of various knitted items like sweaters and socks using mathematical models and advanced simulation techniques. Significant advancements have been achieved in this area. Visual simulations of these fabrics now boast enhanced details, textures, and colors, closely resembling real‐world counterparts. Additionally, physical simulations have improved the movement and deformation aspects of virtual knitted fabrics, enhancing their realism. However, challenges remain. One major concern is the computational demands of these simulations, which can hinder real‐time interactions. Another issue is refining the accuracy of material models, especially when simulating diverse fibers and textures. This paper aims to review the current state of knitted fabric simulation and speculate on its future directions. [ABSTRACT FROM AUTHOR]

Various rotation operations are required in paleomagnetism. These include transformations to the geographic coordinate system, tilt correction, and finding virtual geomagnetic poles. Different methods have been used for each rotation operation—graphical manipulation on stereonet, matrix calculation, and spherical trigonometry—and computer software has been developed based on these methods. Quaternions, which are commonly used in three-dimensional computer graphics, can handle rotations about arbitrary axes and provide a unified description of the various rotation operations in paleomagnetism. The conversion from a sample coordinate system to the geographic coordinate system depends on orientation methods that vary by sample type and laboratory. Traditionally, coordinate transformations have been computed using rotation matrices of Euler angles based on stereonet manipulation, but quaternions can flexibly accommodate samples oriented by different conventions. Tilt correction can be expressed as a single rotation around the strike direction of the formation. Virtual geomagnetic poles can be obtained by a single quaternion rotation instead of complicated spherical trigonometry. Python functions are provided for all of the rotation operations discussed in this paper, so the readers can incorporate these functions into their own programs to perform rotations using quaternions. [ABSTRACT FROM AUTHOR]

Image and video inpainting is a classic problem in computer vision and computer graphics, aiming to fill in the plausible and realistic content in the missing areas of images and videos. With the advance of deep learning, this problem has achieved significant progress recently. The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting. Specifically, we sort existing methods into different categories from the perspective of their high-level inpainting pipeline, present different deep learning architectures, including CNN, VAE, GAN, diffusion models, etc., and summarize techniques for module design. We review the training objectives and the common benchmark datasets. We present evaluation metrics for low-level pixel and high-level perceptional similarity, conduct a performance evaluation, and discuss the strengths and weaknesses of representative inpainting methods. We also discuss related real-world applications. Finally, we discuss open challenges and suggest potential future research directions. [ABSTRACT FROM AUTHOR]

A large number of ancient mosaics are no longer available to us because they have been destroyed by erosion, earthquakes, looting, or even used as materials in newer construction. To make things worse, among the small fraction of mosaics that have been recovered, many are damaged or incomplete. Therefore, it is of interest to explore how the mosaics may have appeared originally by using virtual reconstruction. This has traditionally been done manually and more recently using computer graphics programs but always by humans. In the last few years, artificial intelligence (AI) has made impressive progress in the generation of images from text descriptions and reference images. State-of-the-art AI tools such as DALL-E2 can generate high-quality images from text prompts and can take a reference image to guide the process. In August 2022, DALL-E2 launched a new feature called outpainting that takes as input an incomplete image and a text prompt and then generates a complete image filling in the missing parts. In this paper, we explore whether this innovative technology can be used to perform virtual reconstruction of mosaics with missing parts. Hence, a set of ancient mosaics have been selected and DALL-E2 has been used to create virtual reconstructions; the results are promising, showing that AI is able to interpret the key features of the mosaics and is able to produce virtual reconstructions that capture the essence of the scene. However, in some cases AI does not reproduce some details or geometric forms or introduces elements that are not consistent with the rest of the mosaic. This suggests that when AI image generation technology matures in the next few years, it could be a valuable tool to create virtual reconstructions of mosaics in the future. [ABSTRACT FROM AUTHOR]

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]

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]

Cubic Bézier curves are widely used in computer graphics and geometric modeling, favored for their intuitive design and ease of implementation. However, self-intersections within these curves can pose significant challenges in both geometric modeling and analysis. This paper presents a comprehensive approach to detecting and computing self-intersections of cubic Bézier curves. We introduce an efficient algorithm that leverages both the geometric properties of Bézier curves and numerical methods to accurately identify intersection points. The self-intersection problem of cubic Bézier curves is firstly transformed into a quadratic problem by eliminating trivial solutions. Subsequently, this quadratic system is converted into a linear system that may be easily analyzed and solved. Finally, the parameter values corresponding to the self-intersection points are computed through the solution of the linear system. The proposed method is designed to be robust and computationally efficient, making it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

In this short paper, we analyze the problem of finding the triangular barycentric coordinates of an interpolated ray hitting a given point. This task, which we term the inverse barycentric displacement problem, is general and useful in geometry processing and computer graphics. Concrete applications of the solution include the construction of displacement maps and texture baking. We derive the set of complete, closed-form solutions and discuss the number and existence of solutions. We close with a discussion of implementation-oriented optimizations and a few example applications. [ABSTRACT FROM AUTHOR]

Automatic anomaly detection plays a critical role in surveillance systems but requires datasets comprising large amounts of annotated data to train and evaluate models. Gathering and annotating these data is a labor-intensive task that can become costly. A way to circumvent this is to use synthetic data to augment anomalies directly into existing datasets. This far more diverse scenario can be created and come directly with annotations. This however also poses new issues for the computer-vision engineer and researcher end users, who are not readily familiar with 3D modeling, game development, or computer graphics methodologies and must rely on external specialists to use or tweak such pipelines. In this paper, we extend our previous work of an application that synthesizes dataset variations using 3D models and augments anomalies on real backgrounds using the Unity Engine. We developed a high-usability user interface for our application through a series of RITE experiments and evaluated the final product with the help of deep-learning specialists who provided positive feedback regarding its usability, accessibility, and user experience. Finally, we tested if the proposed solution can be used in the context of traffic surveillance by augmenting the train data from the challenging Street Scene dataset. We found that by using our synthetic data, we could achieve higher detection accuracy. We also propose the next steps to expand the proposed solution for better usability and render accuracy through the use of segmentation pre-processing. [ABSTRACT FROM AUTHOR]

Designing virtual characters that are capable of reflecting a sense of personality is a key goal in research and applications in virtual reality and computer graphics. More and more research efforts are dedicated to investigating approaches to construct a diverse, equitable, and inclusive metaverse by infusing expressive personalities and styles into virtual avatars. While most previous work focused on exploring variations in virtual characters' dynamic behaviors, characters' visual appearance plays a crucial role in affecting their perceived personalities. This paper presents a series of experiments evaluating the effect of virtual characters' outfits on their perceived personality. Based on the related psychology research conducted in the real world, we determined a set of outfit factors likely to reflect personality in virtual characters: color, design, and type. As a framework for our study, we used the "Big Five" personality model for evaluating personality traits. To test our hypothesis, we conducted three perceptual experiments to evaluate the outfit parameters' contributions to the characters' personality. In our first experiment, we studied the color factor by varying color hue, saturation, and value; in the second experiment, we evaluated the impact of different neckline, waistline, and sleeve designs; and in our third experiment, we examined the personality perception of five outfit types: professional, casual, fashionable, outdoor, and indoor. Significant results offer guidance to avatar designers on how to create virtual characters with specific personality profiles. We further conducted a verification test to extend the application of our findings to animated virtual characters in augmented reality (AR) and virtual reality (VR) settings. Results confirmed that our findings can be broadly applied to both static and animated virtual characters in VR and AR environments that are commonly used in games, entertainment, and social networking scenarios. [ABSTRACT FROM AUTHOR]

From the information forensics point of view, it is important to correctly classify between natural images (outputs of digital cameras) and computer-graphics images (outputs of advanced graphics rendering engines), so as to know the source of the images and the authenticity of the scenes described in the images. It is challenging to achieve good classification performance when the forensic classifier is tested on computer-graphics images generated by unknown rendering engines and when we have a limited number of training samples. In this paper, we propose two simple yet effective methods to improve the classification performance under such challenging situations, respectively based on data augmentation and the combination of local and global prediction results. Compared with existing methods, our methods are conceptually simple and computationally efficient, while achieving satisfying classification accuracy. Experimental results on datasets comprising computer-graphics images generated by four popular and advanced graphics rendering engines demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Loop subdivision is a significant surface scheme with wide applications in fields like computer graphics and wavelet. As a type of stationary scheme, Loop subdivision cannot adjust the limit surface directly. In this paper, we present a new way to solve this problem by proposing a symmetric non-stationary Loop subdivision based on a suitable iteration. This new scheme can be used to adjust the limit surfaces freely and thus can generate surfaces with different shapes. For this new scheme, we show that it is C 2 convergent in the regular part of mesh and is at least tangent plane continuous at the limit positions of the extraordinary points. Additionally, we present a non-uniform generalization of this new symmetric non-stationary subdivision so as to locally control the shape of the limit surfaces. More interestingly, we present the limit positions of the initial points, both for the symmetric non-stationary Loop subdivision and its non-uniform generalization. Such limit positions can be used to interpolate the initial points with different valences, generalizing the existing result. Several numerical examples are given to illustrate the performance of the new schemes. [ABSTRACT FROM AUTHOR]

In this paper, a method of stroke‐based rendering is proposed for image representation and reconstruction. The proposed method involves compositing a set of ellipses that greatly vary in location, size, rotation angle, color, and opacity. This study proves that alpha compositing is differentiable if a two‐dimensional Gaussian function is used to draw a solid ellipse. The gradient method can then be employed for automatically identifying the parameters of each ellipse such that the difference between the input image and the composited image is minimized. Experimental results indicate that the proposed method can represent various types of images including portraits, landscapes, buildings, street scenes, artificial objects, medical images etc. The proposed method can particularly render the most details and significant features of an input image with fewer strokes compared to other stroke‐based rendering algorithms. This study also demonstrates that the proposed method can be applied in painting style simulation and sparse‐view computed tomography imaging. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring. [ABSTRACT FROM AUTHOR]

In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world. Therefore, this paper proposed a method to automatically extract cloth stiffness values from video scenes, and then they are applied as material properties for virtual cloth simulation. We propose the use of deep learning (DL) models to tackle this issue. The Transformer model, in combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, and VGG19, stands as a leading choice for video classification tasks. Position-Based Dynamics (PBD) is a computational framework widely used in computer graphics and physics-based simulations for deformable entities, notably cloth. It provides an inherently stable and efficient way to replicate complex dynamic behaviors, such as folding, stretching, and collision interactions. Our proposed model characterizes virtual cloth based on softness-to-stiffness labels and accurately categorizes videos using this labeling. The cloth movement dataset utilized in this research is derived from a meticulously designed stiffness-oriented cloth simulation. Our experimental assessment encompasses an extensive dataset of 3840 videos, contributing to a multi-label video classification dataset. Our results demonstrate that our proposed model achieves an impressive average accuracy of 99.50%. These accuracies significantly outperform alternative models such as RNN, GRU, LSTM, and Transformer. [ABSTRACT FROM AUTHOR]

The paper explores the successful merging of expertise in 'funerary archaeology' and 'digital archaeology' research domains. The Author first conducts a terminological analysis to establish a framework for both subjects based on their unique theoretical and methodological backgrounds and then highlights common methodological issues from the 1960s up to today. The result is a complex scenario in which the main applications include spatial analysis techniques and the GIS-based approach for the study of the relationship between cemeteries, settlements and territory; computer graphics and Virtual Reality techniques for the reconstruction of specific funerary structures and burial typologies; multivariate statistical analyses for the automatic classification of grave goods and their chronological ordering; modelling and simulation techniques to mimic features and behaviours of past ritual practices. [ABSTRACT FROM AUTHOR]

A spline is a sufficiently smooth piecewise curve. B-spline functions are powerful tools for obtaining computational outcomes. They have also been utilized in computer graphics and computer-aided design due to their flexibility, smoothness and accuracy. In this paper, a numerical procedure dependent on the cubic B-spline (CuBS) for the time fractional diffusion wave equation (TFDWE) is proposed. The standard finite difference (FD) approach is utilized to discretize the Atangana–Baleanu fractional derivative (ABFD), while the derivatives in space are approximated through the CuBS with a θ -weighted technique. The stability of the propounded algorithm is analyzed and proved to be unconditionally stable. The convergence analysis is also studied, and it is of the order O (h 2 + (Δ t) 2) . Numerical solutions attained by the CuBS scheme support the theoretical solutions. The B-spline technique gives us better results as compared to other numerical techniques. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the properties of timelike and spacelike shifted-knots Bézier surfaces in Minkowski space- E13. These surfaces are commonly used in mathematical models for surface formation in computer science for computer-aided geometric design and computer graphics, as well as in other fields of mathematics. Our objective is to analyze the characteristics of timelike and spacelike shifted-knots Bézier surfaces in Minkowski space- E13. To achieve this, we compute the fundamental coefficients of shifted-knots Bézier surfaces, including the Gauss-curvature, mean-curvature, and shape-operator of the surface. Furthermore, we present numerical examples of timelike and spacelike bi-quadratic (m = n = 2) and bi-cubic (m = n = 3) shifted-knots Bézier surfaces in Minkowski space- E13 to demonstrate the applicability of the technique in Minkowski space. [ABSTRACT FROM AUTHOR]

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]

The mathematics curriculum has become more dynamic, rigorous, and interdisciplinary in recent years. Therefore, complementing the academic concepts with practical and experiential learning is essential to optimize the learning outcomes. When daunting scientific principles and topics are implemented by traditional diagrams and equations, most students have trouble visualizing; restricting their ability to grasp the idea in detail. Augmented reality has recently become an engineering education method to teach abstract concepts as it enhances the visualization and understanding of technical concepts. In this paper, an augmented reality-based application, 'Hyperspace', was developed to enhance undergraduate students' spatial skills and conceptual knowledge in trigonometry. Hyperspace provides students with features such as augmenting three-dimensional trigonometric functions. These trigonometric functions are dynamically generated using procedural content generation algorithms and computer graphics in augmented reality. An experimental study was conducted to evaluate the effectiveness of Hyperspace on the spatial skills and conceptual knowledge of the students. In total, 127 first-year engineering students took part in the study, and they were randomly assigned to two groups. The students of one group were taught using an AR-based application, and students of the other group were taught using a traditional approach. The experimental outcomes indicate that the augmented reality-based application 'Hyperspace' has significantly enhanced the spatial skills and conceptual knowledge of students when learning about trigonometry. [ABSTRACT FROM AUTHOR]

Three-dimensional reconstruction from point clouds is an important research topic in computer vision and computer graphics. However, the discrete nature, sparsity, and noise of the original point cloud contribute to the results of 3D surface generation based on global features often appearing jagged and lacking details, making it difficult to describe shape details accurately. We address the challenge of generating smooth and detailed 3D surfaces from point clouds. We propose an adaptive octree partitioning method to divide the global shape into local regions of different scales. An iterative loop method based on GRU is then used to extract features from local voxels and learn local smoothness and global shape priors. Finally, a moving least-squares approach is employed to generate the 3D surface. Experiments demonstrate that our method outperforms existing methods on benchmark datasets (ShapeNet dataset, ABC dataset, and Famous dataset). Ablation studies confirm the effectiveness of the adaptive octree partitioning and GRU modules. [ABSTRACT FROM AUTHOR]