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Texture editing is a crucial task in 3D modelling that allows users to automatically manipulate the surface materials of 3D models. However, the inherent complexity of 3D models and the ambiguous text description lead to the challenge of this task. To tackle this challenge, we propose ITEM3D, a Texture Editing Model designed for automatic 3D object editing according to the text Instructions. Leveraging the diffusion models and the differentiable rendering, ITEM3D takes the rendered images as the bridge between text and 3D representation and further optimizes the disentangled texture and environment map. Previous methods adopted the absolute editing direction, namely score distillation sampling (SDS) as the optimization objective, which unfortunately results in noisy appearances and text inconsistencies. To solve the problem caused by the ambiguous text, we introduce a relative editing direction, an optimization objective defined by the noise difference between the source and target texts, to release the semantic ambiguity between the texts and images. Additionally, we gradually adjust the direction during optimization to further address the unexpected deviation in the texture domain. Qualitative and quantitative experiments show that our ITEM3D outperforms the state‐of‐the‐art methods on various 3D objects. We also perform text‐guided relighting to show explicit control over lighting. Our project page: https://shengqiliu1.github.io/ITEM3D/. [ABSTRACT FROM AUTHOR]

The texture mapping technique based on irregular surfaces is widely used in many fields such as film and television, industry and games, etc. In order to adapt to the rapid development in the field of computer graphics and further enhance the uniformity and effectiveness of the texture mapping effect, a triangular mesh simplified texture mapping technique based on the optimized spring-fingertip is proposed. Firstly, a complex two-dimensional graph is established through the spring-fingertip model, which is parameterized and normalized to reduce the deformation of the texture; subsequently, the concept of triangular mesh simplification is introduced to optimize the model timeliness, which replaces the traditional way of folding the edges; finally, the weight of each edge is further analyzed through local curvature calculation. In order to verify the effectiveness of the texture mapping method, simulation and analysis experiments are done, and the experimental results show that the accuracy of the model reaches 99.25%, which is an average improvement of 7.19% relative to the remaining four models. Therefore, the texture mapping model based on optimized triangular mesh effectively improves the realism of the mapping effect and reduces the computational burden of the model. [ABSTRACT FROM AUTHOR]

Mapping skills between different codes to represent numerical information, such as number symbols (i.e., verbal number words and written digits) and non‐symbolic quantities, are important in the development of the concept of number. The aim of the current study is to investigate children's mapping skills by incorporating another numerical code that emerges at early stages in development, finger patterns. Specifically, the study investigates (i) the order in which mapping skills develop and the association with young children's understanding of cardinality; and (ii) whether finger patterns are processed similarly to symbolic codes or rather as non‐symbolic quantities. Preschool children (3‐year‐olds, N = 113, Mage = 40.8 months, SDage = 3.6 months; 4‐year‐olds, N = 103, Mage = 52.9 months, SDage = 3.4 months) both cardinality knowers and subset‐knowers, were presented with twelve tasks that assessed the mappings between number words, Arabic digits, finger patterns, and quantities. The results showed that children's ability to map symbolic numbers precedes the understanding that such symbols reflect quantities, and that children recognize finger patterns above their cardinality knowledge, suggesting that finger patterns are symbolic in essence. Research Highlights: Children are more accurate in mapping between finger patterns and symbols (number words and Arabic digits) than in mapping finger patterns and quantities, indicating that fingers are processed holistically as symbolic codes.Children can map finger patterns to symbols above their corresponding cardinality level even in subset‐knowers.Finger patterns may play a role in the process by which children learn to map symbols to quantities.Fingers patterns' use in the classroom context may be an adequate instructional and diagnostic tool. [ABSTRACT FROM AUTHOR]

We present an automated framework founded on texture transfer, facilitating the substitution of textures in garment images with specified ones for applications in garment design and online presentation. In contrast to previous methodologies, our approach achieves seamless texture transfer from a single image while preserving fold variations and shadow intricacies. Given a garment image and a texture image, we initially extract pixel-aligned features from the garment image and construct a parametric model of the garment through spatial sampling. The mesh structure is subsequently validated employing the Marching Cube algorithm. We then enhance the model quality through mesh optimization using a variational approach. Finally, we optimize parallax mapping to execute the texture transfer from the source texture image. Experimental results convincingly demonstrate the effectiveness of our method in achieving texture transfer in garment images while maintaining the fidelity of folds and shadows. [ABSTRACT FROM AUTHOR]

Summer learning loss is a perennial concern for educators and parents alike. However, researchers have recently questioned whether summer learning loss is just a statistical artifact driven by how achievement is measured across the school year. In this study, we empirically investigated a plausible critique of summer learning loss research, namely that students do not put forth their best effort on the fall test compared with the spring test. While we cannot conclude based on our findings that students do in fact lose ground during the summer, we did not find evidence that seasonal differences in test effort are a main driver of summer learning patterns estimated with MAP Growth assessments. [ABSTRACT FROM AUTHOR]

The study of evolutionary rates and patterns is the key to understand how natural selection shaped the current and past diversity of phenotypes. Phylogenetic comparative methods offer an array of solutions to undertake this challenging task, and help understanding phenotypic variation in full in most circumstances. However, complex, three-dimensional structures such as the skull and the brain serve disparate goals, and different portions of these phenotypes often fulfil different functions, making it hard to understand which parts truly were recruited by natural selection. In the recent past, we developed tools apt to chart evolutionary rate and patterns directly on three-dimensional shapes, according to their magnitude and direction. Here, we present further developments of these tools, which now allow to restitute the mapping of rates and patterns with full biological realism. The tools are condensed in a new R software package. Evolutionary rates embody the velocity of evolution. Parcellating different velocities across the phenotype is difficult. RRmorph resolves this conundrum by charting evolutionary patterns on 3D shapes, according to their magnitude and direction. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering them unsuitable for deployment in portable devices. This paper presents a compact fault diagnosis model that integrates a self-attention SqueezeNet architecture with a hybrid texture representation technique utilizing empirical mode decomposition (EMD) and a gammatone spectrogram (GS) filter. In the model, the dominant signal is first isolated from the audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD, and subsequently, the dominant signals are transformed into 2D texture maps using the GS filter. These generated texture maps feed as input into the modified self-attention SqueezeNet classifier, featuring reduced model width and depth, for training and validation. Different attention modules were tested in the paper, including the self-attention, channel attention, spatial attention, and convolutional block attention module (CBAM). The models were tested on the MIMII and ToyADMOS datasets. The experimental results demonstrated that the self-attention mechanism with SqueezeNet achieved an accuracy of 97% on the previously unseen MIMII and ToyADMOS datasets. Furthermore, the proposed model outperformed the SqueezeNet attention model with other attention mechanisms and state-of-the-art deep architectures, exhibiting a higher precision, recall, and F1-score. Lastly, t-SNE is applied to visualize the features of the self-attention SqueezeNet for different fault classes of both MIMII and ToyADMOS. [ABSTRACT FROM AUTHOR]

How complex 3D tissue shape emerges during animal development remains an important open question in biology and biophysics. Here, we discover a mechanism for 3D epithelial shape change based on active, in-plane cellular events that is analogous to inanimate "shape programmable" materials, which undergo blueprinted 3D shape transformations from in-plane gradients of spontaneous strains. We study eversion of the Drosophila wing disc pouch, when the epithelium transforms from a dome into a curved fold, quantifying 3D tissue shape changes and mapping spatial patterns of cellular behaviors on the evolving geometry using cellular topology. Using a physical model inspired by shape programming, we find that active cell rearrangements are the major contributor to pouch eversion and validate this conclusion using a knockdown of MyoVI, which reduces rearrangements and disrupts morphogenesis. This work shows that shape programming is a mechanism for animal tissue morphogenesis and suggests that patterns in nature could present design strategies for shape-programmable materials. [ABSTRACT FROM AUTHOR]

AbstractThis article presents an alternative general model of democracy to traditional minimalist (electoral and liberal) and deliberative theories by drawing from critical realist philosophical thought. Realist or real democracy is proposed as a societal system where preconditions exist to fully empower all citizens to realize their individual capabilities. This includes empowerment opportunities at the ballot box - but also other preconditions such as health, educational and living standards. Real democracy also separates democratic preconditions from democratic outcomes. Democratic outcomes require the absence of inequalities in power as result from the contingent interaction preconditions and human agency. The new approach is argued to provide a more holistic and dynamic concept of democracy which is also more grounded in the everyday experience of the citizen compared to traditional approaches. It reconnects democracy with theories of human development and enables a new classification of political regimes and conception of democratic politics. An initial empirical mapping of patterns of global real democracy shows evidence of rising preconditions since 1990 – but stagnation since 2010 and some signs of decline. Democratic outcomes by socio-economic position saw the most notable decline. [ABSTRACT FROM AUTHOR]

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning. [ABSTRACT FROM AUTHOR]

3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling. [ABSTRACT FROM AUTHOR]

• Bilateral somatosensory evoked spikes (SESs) (previously reported only in children) were detected in spontaneous MEG recordings of 10/30 adult subjects. • The first peak of SESs was considered to be identical to the somatosensory evoked field (SEF) M2 in latency, equivalent current dipole (ECD) position, and ECD orientation. • M2 amplitude of SEFs was significantly greater in subjects with SESs. Somatosensory evoked spikes (SESs) have been reported only in children aged under 14 years and are considered as an age-dependent phenomenon. However, we detected SESs in adult patients with epilepsy using magnetoencephalography (MEG). The present study investigated whether MEG can detect SESs in normal adults. Spontaneous MEG was recorded during measurement of somatosensory evoked fields (SEFs) for bilateral electrical median nerve stimuli in 30 healthy adults. Bilateral SESs were observed in 10 adults but none in the other 20 subjects. SESs consisted of one or two peaks, and the first peak latency corresponded to that of the second peak (M2) of SEFs. The first SES peak was identical to the M2 in isofield map pattern, as well as location and orientation of the equivalent current dipole (ECD). M2 ECD strength in the 10 subjects with SESs was larger (p <0.0001) than in 20 without SESs. All-or-nothing detection of bilateral SESs by MEG in normal adults must depend on the signal-to-noise issue of symmetrical SEFs and background brain activity. Our results further confirm the higher sensitivity of MEG compared to scalp EEG for the detection of focal cortical sources tangential to the scalp such as SESs. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

The categories and styles of garment are constantly diversifying. For designers, it is a pressing issue to evaluate how different fabrics will look on them in a timely manner for users. In this paper, we present a novel garment texture transfer framework from a single person image. Based on the garment model constructed in the person image, we render the texture to the model surface using parallax mapping. To determine the relative positions of the garments when exporting the images, we calculate the contour center moments of the garment mask and the center of mass coordinates of the 3D model and use their consistency to perform position calibration. Finally, we align the rendered garment image with the figure image to obtain the final transfer effect. Experiments demonstrated that our method is robust to different character pose with different garments and background. Qualitative experimental results show that our method accurately and realistically relocates the texture of the garment in the image of the person while preserving the original folds of the garment. Quantitative comparisons with other methods show that our method is optimal in several metrics. [ABSTRACT FROM AUTHOR]

Despite the growing recognition of the importance of street vendors and informal markets for urban life and the economy globally, research attention has tended to focus on essential products such as food or clothes and paid less attention to cultural products, such as antiques where potential gender, and other socio-demographic, differences in vending and buying behaviours may systematically differ. To explore these issues, this study employed spatial–temporal behaviour mapping (STBM) and field observations (n = 8587) at Beijing's Panjiayuan antiques "ghost market" a term reflecting its previously illegal/underground and mobile nature. We monitored four representative sites within the market, four times a day for six consecutive days. The data included five categories of behaviour, four age groups, and two genders. Data were recorded and analysed using ArcGIS. Results showed that, unlike common marketplaces, the antique market is primarily frequented by male vendors and buyers, replicating patterns seen in ancient Chinese paintings of men being involved in the trading of "non-essentials". Nonetheless, we found differing gender balances depending on the time and day of the week, different age groups, and spatial settings in different market areas. We summarise the nuanced relationships between the emerging gender spatio-temporal behaviour patterns and three key factors, spatial characteristics, physical activities and the social division of labour, in the Chinese context. The study reveals how the varied spatial–temporal patterns of a large antique "ghost market" raise long-established issues of equity and inclusivity and provide empirical insights that could inform decision-making and urban planning, such as better use of dynamic lighting provision to encourage a more gender-balanced experience. [ABSTRACT FROM AUTHOR]

Collective spin arrangements manifest diverse spin textures, encompassing ferromagnetism, antiferromagnetism, and chiral vortices. However, mapping these spin textures in ultrathin magnetic multilayers has remained elusive. A reconfigurable chiral spintronic terahertz emission method is introduced through investigations on a model system of synthetic antiferromagnet and the spin information of individual ferromagnet (FM) layers is extracted. Upon femtosecond photoexcitation of the synthetic antiferromagnet (FM1/Ru/FM2), the ferromagnets generate a pair of linearly polarized ultrafast spin currents, which, after relaxation at the FM/Ru interface, emit corresponding THz pulses. The Ruderman–Kittel–Kasuya–Yosida interactions between the two FMs give rise to magnetic‐field‐controlled spin textures causing a spin relaxation imbalance at the interfaces and induce a phase shift between the orthogonal components of the emitted terahertz fields, consequently reconfiguring the polarization of the emitted terahertz field from linear to circular. This approach offers a novel means of investigating electronic and magnetic states in ultrathin spintronic heterostructures, low‐dimensional quantum materials, topological insulators, and Weyl semimetals. Furthermore, it enables the exploration of spin textures, including skyrmions, merons, and solitons. [ABSTRACT FROM AUTHOR]

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks. [ABSTRACT FROM AUTHOR]

When it became clear that cause-related marketing (CaRM) is a primary driver of a company's marketing strategy, CaRM as a research domain developed rapidly. Despite the fact that CaRM has received a lot of attention, only a few authors have done bibliometric analysis on it. As a result, an additional bibliometric analysis of CaRM is required to bring together the additional contributions, developments, and current research lines. Thus, this research aims to use bibliometric analysis to better understand the CaRM concept in marketing literature. In this study, 443 articles from Scopus, a widely used electronic database, are retrieved for the years 2000 to 2020. The authors used VOSviewer to perform citation analysis, co-citation analysis, keyword analysis, and bibliographic coupling. Our findings revealed that the field's development is dominated by authors and institutes from the United States and Europe. The presence of Asian countries indicates that the topic of research is becoming increasingly important. The data support the claim that the majority of current ideas in the field are derived from publications published in prestigious journals. Bibliographic coupling resulted in the identification of five clusters: (1) mechanism of CaRM, (2) attributes of CaRM, (3) CaRM and consumer behavior, (4) role of different factors in the effectiveness of CaRM, (5) cause and brand in CaRM. The findings of this study will be relevant not only to scholars working in the CaRM field but also to practitioners and policymakers who want to improve their understanding of CaRM. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM's tremendous potential in optimizing the reliability design of APS. [ABSTRACT FROM AUTHOR]

The spatial distribution of extreme precipitation significantly affects flow‐producing processes and flooding. Previous studies on heavy rainfall have mainly emphasized the temporal distribution characteristics, with little emphasis on rainfall spatial patterns. We objectively classified 3‐h scale extreme precipitation spatial patterns (EPSPs) in Guangdong Province, China. We calculated the importance of the influencing factors on EPSPs, analysed weather backgrounds corresponding to various EPSPs, and explained the causes of extreme precipitation. We found that the incidence of most EPSPs increased significantly over this 40‐year period, and this increase has been particularly pronounced since the beginning of the 21st century. The Pacific Decadal Oscillation (PDO) was found to be the main factor influencing the extreme precipitation events (EPEs) in western Guangdong, and the weakening of the PDO contributed to the occurrence of EPEs in these areas. Urbanization was the main factor contributing to the increase in EPEs in the southern and coastal areas of Guangdong. The EPSPs in central Guangdong were caused by a southwest jet stream and topographic uplift. Extreme precipitation in southern and coastal Guangdong was mainly triggered by the convergent shear of southwesterly winds. The EPSPs over western Guangdong were caused by the low vortex in western Guangdong and the influx of large amounts of water vapour from the southern ocean. This study has provided new ideas for the study of the formation process and mechanism of localized heavy precipitation as well as an important reference for the simulation of runoff in coastal areas. [ABSTRACT FROM AUTHOR]

Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse sizes, thus elevating the performance and precision of target detection algorithms. To further scrutinize the YOLO-v3 joint algorithm's performance in urban traffic detection, P-R curves were plotted for various loss types on the NEU-DET dataset. Comparative analysis of these curves highlights the optimized algorithms' superiority in detecting various types of losses in urban model completeness. Additionally, practical application analysis revealed that the optimized monitoring results outperform the detection time of the original YOLO-v3_means++ network model on FP_GA. Notably, post-processing with C-FENCE can reduce average single-frame image detection time to 2.01 seconds, while convolutional degree-level fusion with the BN layer cuts it down to 2.25 seconds. In summary, the FP_GA-based YOLO-v3_means++ network algorithm offers superior detection capabilities, and the multi-objective evolutionary algorithm's optimization of the YOLO-v3 model enhances target detection performance and precision. [ABSTRACT FROM AUTHOR]

This work will first focus on the various technical aspects of the design of photorealistic 3D renderings of architectural objects. Special attention will be given to the role of new technologies, such as photogrammetry or texture mapping, in the preservation and enhancement of the architectural heritage. The problem of restoring the visual appearance of 3D architectural heritage objects through texture mapping will also be addressed. Hence, an algorithm that achieve a piecewise texture mapping by minimizing length distortion based on artificial neural networks (ANN) and the theory of elasticity was presented. To begin, a surface triangulation S (comprising triangular facets and a boundary) is created. The obtained mesh is then partitioned into disjoint pieces, with each interior vertex belonging to a single piece. Each subsurface, corresponding to one of the obtained pieces, is then mapped independently from the others by imposing local length-preservation constraints and barycentric coordinates of the interior mesh vertices. The proposed method aims to achieve uniform and seamless texturing, allowing for photorealistic representation of 3D models of heritage buildings and cities. This work provides an effective mathematical framework to obtain geometrically precise texture generating process on surfaces of arbitrary architectural topology.

AbstractIn the present work, air-water two-phase flow pattern and pressure drop in vertical upward annular channel with different diameters are experimentally studied. Two concentric annular test sections with the same hydraulic diameter of 4.6 mm are used in the experiments. The outer diameter and inner diameter corresponding to the two test sections are 18.60 and 12.30, and 86.75 and 80.45, respectively. Bubble flow, cap-slug flow, cap-churn flow, and annular flow were found in both the test sections and the flow pattern map is developed. It is found that the superficial gas velocity has an important effect on the transition of the flow patterns. Pressure drop correlations are reviewed and a total of 12 correlations are collected, including 4 homogeneous flow model (HFM)-based correlations and 8 separated flow model-based correlations. The existing correlations for estimating pressure drop were tested against the measured data. The comparisons show that no correlation studied here provides satisfactory agreement with the whole database. The Chisholm correlation provides the best accuracy with 20.9% for the small test section and 21.5% for large test section. It is also found that the existing models based on separated flow method predict frictional pressure drop with higher accuracy than those based on HFM. The result of this study highlights the requirement of a new method for calculating the frictional pressure drop in annular channels. [ABSTRACT FROM AUTHOR]

Hybrid zones have been viewed as an opportunity to see speciation in action. When hybrid zones are replicated, it is assumed that if the same genetic incompatibilities are maintaining reproductive isolation across all instances of secondary contact, those incompatibilities should be identifiable by consistent patterns in the genome. In contrast, changes in allele frequencies due to genetic drift should be idiosyncratic for each hybrid zone. To test this assumption, we simulated 20 replicates of each of 12 hybrid zone scenarios with varied genetic incompatibilities, rates of migration, selection and different starting population size ratios of parental species. We found remarkable variability in the outcomes of hybridisation in replicate hybrid zones, particularly with Bateson–Dobzhansky–Muller incompatibilities and strong selection. We found substantial differences among replicates in the overall genomic composition of individuals, including admixture proportions, inter‐specific ancestry complement and number of ancestry junctions. Additionally, we found substantial variation in genomic clines among replicates at focal loci, regardless of locus‐specific selection. We conclude that processes other than selection are responsible for some consistent outcomes of hybridisation, whereas selection on incompatibilities can lead to genomically widespread and highly variable outcomes. We highlight the challenge of mapping between pattern and process in hybrid zones and call attention to how selection against incompatibilities will commonly lead to variable outcomes. We hope that this study informs future research on replicate hybrid zones and encourages further development of statistical techniques, theoretical models and exploration of additional axes of variation to understand reproductive isolation. [ABSTRACT FROM AUTHOR]

In response to the growing societal awareness of the critical role of healthy soils, there has been an increasing demand for accurate and high-resolution soil information to inform national policies and support sustainable land management decisions. Despite advancements in digital soil mapping and initiatives like GlobalSoilMap, quantifying soil variability and its uncertainty across space, depth and time remains a challenge. Therefore, maps of key soil properties are often still missing on a national scale, which is also the case in the Netherlands. To meet this challenge and fill this data gap, we introduce BIS-4D, a high-resolution soil modeling and mapping platform for the Netherlands. BIS-4D delivers maps of soil texture (clay, silt and sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity and their uncertainties at 25 m resolution between 0 and 2 m depth in 3D space. Additionally, it provides maps of soil organic matter and its uncertainty in 3D space and time between 1953 and 2023 at the same resolution and depth range. The statistical model uses machine learning informed by soil observations amounting to between 3815 and 855 950, depending on the soil property, and 366 environmental covariates. We assess the accuracy of mean and median predictions using design-based statistical inference of a probability sample and location-grouped 10-fold cross validation (CV) and prediction uncertainty using the prediction interval coverage probability. We found that the accuracy of clay, sand and pH maps was the highest, with the model efficiency coefficient (MEC) ranging between 0.6 and 0.92 depending on depth. Silt, bulk density, soil organic matter, total nitrogen and cation exchange capacity (MEC of 0.27 to 0.78), and especially oxalate-extractable phosphorus (MEC of -0.11 to 0.38) were more difficult to predict. One of the main limitations of BIS-4D is that prediction maps cannot be used to quantify the uncertainty in spatial aggregates. We provide an example of good practice to help users decide whether BIS-4D is suitable for their intended purpose. An overview of all maps and their uncertainties can be found in the Supplement. Openly available code and input data enhance reproducibility and help with future updates. BIS-4D prediction maps can be readily downloaded at 10.4121/0c934ac6-2e95-4422-8360-d3a802766c71. BIS-4D fills the previous data gap of the national-scale GlobalSoilMap product in the Netherlands and will hopefully facilitate the inclusion of soil spatial variability as a routine and integral part of decision support systems. [ABSTRACT FROM AUTHOR]

This study aims to analyze two-phase flow patterns of water and air in a vertical column using experimental tests, and investigate the effect of pulsed gas flow on the characteristics of the Taylor bubble under different inlet conditions. First, three patterns of bubble, slug, and churn were observed with above hundred tests and a flow patterns map was drawn. Then, the effect of the pulsed gas flow on the length of the Taylor bubble was investigated in 150 different experiments. The ranges of superficial velocity of liquid and gas phases were 0.12–0.28 m/s and 0.05–0.25 m/s, respectively. The frequency of pulsed gas feed was 0.25–4 Hz. The results of this study showed that the length of the Taylor bubble in pulsed gas feed decreases with increasing the frequency. In addition, at a fixed gas frequency, the bubble length increases with increasing gas velocity. Present study prove that the pulsed gas flow technique can reduce the length of Taylor bubbles, which will be useful for future industrial applications in two-phase gas-liquid columns. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional structure and texture aware Retinex (FSTAR) model for image decomposition with application to low-light enhancement. First, a novel structure aware measure called Maximum Fractional Difference (MFD) is introduced, which is the maximum of fractional differences of the input image in eight symmetric directions. Then fractional structure and texture aware maps are built based on MFD and Huber function from robust statistics, which are used as weighted matrices in the regularization terms of reflectance and illumination components. To ensure the intrinsic physical constraints of Retinex on two components, two extra penalty terms are incorporated into the objective function of FSTAR to penalize deviations of the estimated components from the corresponding constraints. The FSTAR is solved via an alternating iterative algorithm; i.e., the objective function is minimized with respect to one variable with another variable fixed, and this process is done alternately until termination condition is met. Qualitative and quantitative evaluations of FSTAR on three low-light image datasets, compared to ten state-of-the-art methods, show its superior performance in Retinex decomposition and low-light image enhancement. • Present texture aware measure based on fractional directional difference for texture extraction. • Propose fractional structure-texture aware Retinex model with constraints on two components. • Model is faithful to intrinsic physical meaning of Retinex in theory and easy to solve numerically. • Model has outstanding performance in terms of image decomposition and low-light enhancement. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs). Plain Language Summary: Soil moisture (SM) is important for weather and climate but is often poorly simulated by Land Surface Models (LSMs). One possible reason could be the inappropriate representation of soil texture maps utilized in LSMs since current gridded soil texture maps are often derived from a limited number of in‐situ measurements. In this study, we leverage the benefits of modern satellite products and land surface theories to improve several major global soil texture maps, and use the calibrated soil maps to improve SM simulation in one example LSM. Results show increased sand content over arid areas while the results for clay content show the opposite pattern. The SOC result shows an overall increase over the entire globe but is more evident in dense vegetation land covers. The model simulated SM using the calibrated soil maps generally outperforms those with the baseline soil maps. The improvement is more significant in the experiment with soil maps considering SOC. Our results here provide successful evidence for constraining soil texture data from large‐scale observations. We also show that observation‐oriented calibration on soil texture maps is necessary for a better land surface simulation, which is critically important for the development of ESMs. Key Points: Satellite Soil moisture (SM) data is used to improve the representativeness of two major soil texture data sets at the global scaleSM simulations using the updated soil maps outperform those using the baseline soil texture productsSM improvement is more significant in simulations considering soil organic carbon (SOC) [ABSTRACT FROM AUTHOR]

The paragraph introduces a proposed CP-free OFDM PDMA downlink transmission system. The main focus of the system is to address the capacity limitations caused by the overhead of cyclic prefix in traditional PDMA systems. The transmitter utilizes a pattern mapping unit before CP-free OFDM modulation to enhance system capacity and frequency efficiency. Decision feedback equalization (DFE) is employed in the receiver to eliminate intersymbol interference. The output signals from the DFE are then passed through a CP restoration unit to convert a linear-shifted signal into a cyclic-shifted signals. To assess the system's performance, simulations are conducted, investigating different key parameters such as overload rate, channel condition, and signal-to-noise ratio. The results indicate that, compared to CP OFDM PDMA systems, the proposed CP-free PDMA system significantly enhances system capacity under the same overload rate. Additionally, bit error rate is also evaluated during the simulations. Overall, the paragraph provides an overview of the proposed CP-free OFDM PDMA system, its components, and the simulation-based evaluation of its performance compared to traditional PDMA systems. [ABSTRACT FROM AUTHOR]

In this study, we innovatively propose salience-combined map and gradientlet filter for infrared and visible image fusion. It can enhance the infrared image of the target and also retain more detailed textures. First, our method is based on a multi-scale decomposition framework and gradientlet filter to decompose the source graph into approximate layers and residual layers. The approximate layers preserve smooth areas of the source images without edge blurring. The residual layers reflect the small gradients and noise of the source image. Since the texture part of the residual layer is weak, we introduce a Gamma-enhanced gradient map to complement the texture. The initial fusion image can be obtained by fusing the approximate layers and the residual layers. The salience-combined map directly extracts salient objects from infrared images according to pixel threshold segmentation, and extracts background information other than objects from visible images. Then the salience-combined map is used to guide the initial fusion image to get the final image. In our qualitative analysis, we compared our method against 5 traditional methods and deep learning-based methods. In the quantitative assessment, utilizing 29 pairs of randomly selected source images, our algorithm distinctly showcased absolute superiority across various metrics, including EN, SF, AG, and FD. The aforementioned results affirm that our method ensures the generation of fused images with clear targets and rich details. [ABSTRACT FROM AUTHOR]

This research focuses on the residential patterns that were traced in Amman in the last decades due to the constant influx of migrants. Therefore, mapping the residential patterns that are emerging was seen important to understand the morphology of the area. A mixed-methods approach was used to identify the typologies emerging into the setting. New residential patterns that called for new strategic thinking were captured and analyzed. Enhancing the new image of "celebrated character" that urges new perception of heterogeneous communities and attracting more sustainable residential attributes towards the neighborhood were the two main outcomes of the study. [ABSTRACT FROM AUTHOR]

Microscopic emission estimation based on driving states plays a crucial role in controlling the pollution of on-road vehicles. Existing research has evolved from fitting nonlinear models of driving cycles and emission factors to utilizing neural networks to exploit driving patterns and construct a nonlinear mapping between driving states and emission values. However, due to the small percentage of driving-cycle related to high emissions, it is still challenging to capture the vehicle emission peaks, which lead to the most noteworthy high-emission characteristics being regarded as abnormal disturbances instead. To address the issue, this paper proposes a peak-sensitive microscopic emission estimation framework characterized by sequence-to-sequence learning for on-road vehicles. Sequence-to-sequence learning emphasizes serial pattern mapping from driving sequences to emission sequences to provide more statistical constraints and reduce the estimation uncertainty brought by unstable driving behaviors. Specifically, the framework aims at capturing context features related to high emissions from sequences dynamically in an adaptive and self-learning way and is composed of a driving states embedding module and a dynamic aggregation module. Particularly, an incremental tracking loss (ITL) is proposed to adjust the incremental emissions at adjacent time steps by supervising the differences of the generated sequences, enabling the model to track sudden changes in emissions. Extensive experiments are conducted on the on-board diagnostics (OBD) dataset with 12628 sampling records collected from a heavy-duty diesel vehicle. The results show that the estimation accuracy of our proposed method is significantly better than state-of-the-art methods, and it can effectively capture high-emission peaks. [ABSTRACT FROM AUTHOR]

With increasing interest in understanding and mapping the spin textures within magnetic nanostructures, this work reports a study of the transition from quasi-2D magnetic behavior in thin-film ferromagnetic nanostructures to 3D thick-film nanostructures. A series of arrays of 480 × 250 nm2 elliptical Ni81Fe19 nanomagnets patterned using deep ultraviolet (DUV) lithography with thickness (t) ranging from 20 to 250 nm were studied. It is shown through magnetometry and micromagnetics that as the film thickness increases, the nanomagnets transition from effectively planar 2D magnets, with uniform spin textures extending through the film thickness for t ≤ 50 nm, to 3D nanomagnets with more complex non-uniform 3D spin textures for t ≥ 100 nm. These results demonstrate that the fabrication of thick-film nanomagnets via DUV lithography is a viable route to producing consistent 3D magnetic nanostructures for potential applications, such as magnonics. [ABSTRACT FROM AUTHOR]

Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational, training and testing phases of medical students. This study introduces a technique for enhancing a 3D digital model reconstructed from cone-beam computed tomography images with its real coloured texture using an Intel D435 RGBD camera. This method is based on iteratively projecting the two models onto a 2D plane, identifying their contours and then minimizing the distance between them. Finally, the coloured digital models were displayed in mixed reality through a Microsoft HoloLens 2 and an application to interact with them using hand gestures was developed. The registration error between the two 3D models evaluated using 30,000 random points indicates values of: 1.1 ± 1.3 mm on the x-axis, 0.7 ± 0.8 mm on the y-axis, and 0.9 ± 1.2 mm on the z-axis. This result was achieved in three iterations, starting from an average registration error on the three axes of 1.4 mm to reach 0.9 mm. The heatmap created to visualize the spatial distribution of the error shows how it is uniformly distributed over the surface of the pointcloud obtained with the RGBD camera, except for some areas of the nose and ears where the registration error tends to increase. The obtained results indicate that the proposed methodology seems effective. In addition, since the used RGBD camera is inexpensive, future approaches based on the simultaneous use of multiple cameras could further improve the results. Finally, the augmented reality visualization of the obtained result is innovative and could provide support in all those cases where the visualization of three-dimensional medical models is necessary. [ABSTRACT FROM AUTHOR]

High‐resolution texture maps are essential to render photoreal digital humans for visual effects or to generate data for machine learning. The acquisition of high resolution assets at scale is cumbersome, it involves enrolling a large number of human subjects, using expensive multi‐view camera setups, and significant manual artistic effort to align the textures. To alleviate these problems, we introduce GANtlitz (A play on the german noun Antlitz, meaning face), a generative model that can synthesize multi‐modal ultra‐high‐resolution face appearance maps for novel identities. Our method solves three distinct challenges: 1) unavailability of a very large data corpus generally required for training generative models, 2) memory and computational limitations of training a GAN at ultra‐high resolutions, and 3) consistency of appearance features such as skin color, pores and wrinkles in high‐resolution textures across different modalities. We introduce dual‐style blocks, an extension to the style blocks of the StyleGAN2 architecture, which improve multi‐modal synthesis. Our patch‐based architecture is trained only on image patches obtained from a small set of face textures (<100) and yet allows us to generate seamless appearance maps of novel identities at 6k × 4k resolution. Extensive qualitative and quantitative evaluations and baseline comparisons show the efficacy of our proposed system. (see https://www.acm.org/publications/class-2012) [ABSTRACT FROM AUTHOR]

Analogical reasoning is central to thought and learning. However, previous neuroscience studies have focused mainly on neural substrates for visuospatial and semantic analogies. There has not yet been research on the neural correlates of analogical reasoning on syntactic patterns generated by the syntactic rules, a key feature of human language faculty. The present investigation took an initial step to address this paucity. Twenty-four participants, whose brain activity was monitored by fMRI, engaged in first-order and second-order relational judgments of syntactic patterns as well as simple and complex working memory tasks. After scanning, participants rated the difficulty of each step during analogical reasoning; these ratings were related to signal intensities in activated regions of interest using Spearman correlation analyses. After prior research, differences in activation levels during second-order and first-order relational judgments were taken as evidence of analogical reasoning. These analyses showed that analogical reasoning on syntactic patterns recruited brain regions consistent with those supporting visuospatial and semantic analogies, including the anterior and posterior parts of the left middle frontal gyrus, anatomically corresponding to the left rostrolateral pFC and the left dorsolateral pFC. The correlation results further revealed that the posterior middle frontal gyrus might be involved in analogical access and mapping with syntactic patterns. Our study is the first to investigate the process of analogical reasoning on syntactic patterns at the neurobiological level and provide evidence of the specific functional roles of related regions during subprocesses of analogical reasoning. [ABSTRACT FROM AUTHOR]

3D simulations have been achieved on a flow‐focusing geometry employing the VOF method to study the consequence of viscosity, surface tension, wettability, and geometry on drop generation for the dripping regime. Here the dispersed phase is the PDMS oil (polydimethylsiloxane), and the continuous phase is the water. Simulations were performed at different oil‐to‐water viscosity ratios μoμw of 3, 12, 27, and 50. The interfacial tension between PDMS oil and water is 0.0118 N/m. It has been abridged to 0.008 N/m, 0.005 N/m, and 0.002 N/m, and simulations were performed. The walls of the microchannel are considered to be PMMA surfaces. The contact angle of an oil droplet on the PMMA surface in the presence of water is 140°. The effect of wettability was shown at various contact angles (angle created by water droplet on the PMMA surface in the presence of oil) of 0°, 40°, 90°, 135° and 180°. The frequency of droplet generation (1/s), non‐dimensional droplet length (L/Wc), droplet volume (nl), and droplet velocity (m/s) have been calculated for each of the cases. A flow pattern map has been industrialized classifying the dripping and jetting regimes. A comparison between normal geometry and two constricted geometries (having different orifice lengths) based on the frequency of droplet, non‐dimensional drop length, drop volume, and drop velocity has been made for both dripping and jetting regimes. Prediction of simulated non‐dimensional droplet length has also been made using dimensional analysis. [ABSTRACT FROM AUTHOR]

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest above-ground biomass (AGB) is vital for monitoring changes in forest carbon sinks. However, the spatial heterogeneity of AGB, coupled with inherent uncertainties, poses challenges in acquiring high-quality AGBs. This study introduced a bias-corrected ensemble machine learning (ML) algorithm for AGB downscaling that integrated a ML for AGB mapping with another for residual mapping. The accuracies of six bias-corrected ensemble ML algorithms were evaluated at resolutions of 0.05°, 0.025°, and 0.01°. Moreover, a step-by-step downscaling (SBSD) method was introduced, utilizing bias-corrected ensemble ML algorithms to downscale AGB from 0.1° to 0.05°, 0.025°, and 0.01° resolutions and was compared with the direct downscaling (DD) at three scales. A comparative analysis was conducted in the Daxing'anling Mountains and Xiaoxing'anling Mountains. AGB and corresponding uncertainty maps at three scales were generated using SBSD. The results showed that the efficacy of the XGBoost-based AGB model combined with the random forest-based residual correction model was superior. Spatial patterns in AGB maps generated by SBSD and DD were found to be similar. Notably, SBSD yielded enhanced accuracy in the Daxing'anling Mountains with complex topography, while both performed comparably in the Xiaoxing'anling Mountains with milder topography, highlighting SBSD's advantages in high heterogeneity areas. [ABSTRACT FROM AUTHOR]

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities. [ABSTRACT FROM AUTHOR]

Skyrmions, topologically stable configurations of a three-component vector field with sophisticated textures, have been considered in many contexts, including atomic physics, Bose–Einstein condensates, liquid crystals, and magnetic materials. Although optical counterparts of skyrmions have extensively been studied theoretically and recently demonstrated in the laboratory experiments, their experimental mapping is challenging due to the fine, three-dimensional, and complicated structure of their polarization distributions. Here, we propose and demonstrate a straightforward mapping of the polarization textures of optical Néel-, Bloch-, and anti-skyrmions based on the radiation pressure and direct imprinting of the skyrmion textures on azopolymers. These results not only elucidate the exotic interaction that occurs between topologically protected quasiparticles of light and matter but also provide a simple approach for generation and characterization of optical skyrmions, based on a dual-path polarization shaping configuration with a single spatial light modulator, and their measurements based on the radiation pressure. [ABSTRACT FROM AUTHOR]

Urban three-dimensional modeling involves digitizing geographical elements such as urban ground and architecture, profoundly impacting urban management, planning, and development. In recent years, such models have demonstrated various advantages, but traditional challenges in acquiring comprehensive geographical information persist. The emergence of unmanned aerial vehicle (UAV) oblique photography offers a viable solution, enabling cost-effective acquisition of geographical information and facilitating the construction of three-dimensional urban models. However, UAV-based 3D reconstruction encounters certain issues.Conventional 3D reconstruction typically begins with data collection, involving two-dimensional images or point cloud data acquisition using imagery or LiDAR technology. Subsequent steps include data preprocessing, feature extraction and matching, surface model construction, texture mapping, rendering, and generating three-dimensional models. However, traditional methods exhibit limitations and drawbacks, such as inadequate adaptation to complex scenes, increased computational demands for large-scale scenes, and difficulties with dynamic scenes.Deep learning-based 3D reconstruction methods, like MVSNet, employ deep neural networks to infer scene depth information from multiple-view images, yielding high-quality 3D reconstruction results. However, they rely heavily on prior datasets and auxiliary information, limiting generalization.Neural Radiance Fields (NeRF) combine neural networks with volumetric rendering techniques, excelling in reconstructing objects with high precision and detail, handling dynamic scenes, and addressing areas with sparse viewpoints. However, NeRF's input requires sparse point clouds, commonly obtained using COLMAP, which has limitations. To address these limitations, using NeRF with global Structure-from-Motion (SfM) has emerged as a promising solution. This simultaneous processing of the entire dataset estimates camera trajectories and scene structure from shared features and information among multiple images, resulting in more accurate sparse point clouds and significantly improving image quality. Experimental validation using open-source and self-generated datasets demonstrates that this algorithm markedly enhances surface texture quality compared to traditional 3D reconstruction and NeRF with incremental SfM. In summary, this algorithm enhances 3D reconstruction efficacy and exhibits superior robustness, scalability, and accuracy compared to conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for computing an orthonormal tangent space at every vertex of a triangular mesh provided with texture coordinates, which is essential in normal mapping. Our method employs least-squares estimation of rotation between two sets of corresponding points: one set from the positions of the 1-ring neighbors of a vertex in texture space and the other set from their corresponding positions in world space. This optimal rotation represents a local space transformation that defines the tangent space at the vertex. Consequently, our method ensures that the resulting tangent space is inherently orthonormal, eliminating the need for intricate operations such as averaging and orthonormalization, which are required in the previous methods. In addition, we introduce texture space alignment of triangles to address vertices on seams, which are inevitable in texture mapping to a closed surface mesh. Experimental results demonstrate the effectiveness of the proposed methods in various experiments. [ABSTRACT FROM AUTHOR]

With the rapid progress of computer vision and deep learning techniques, accurately predicting continuous human motions from very few image inputs and generating high-quality 3D human models has become a cutting-edge research direction in this field. Despite the achievements in 2D to 3D conversion techniques, it is still a great challenge to capture coherent movements from limited image frames and generate texture-rich 3D models. In this paper, we propose a 3D clothed human body generation method based on Inter-Frame Motion Prediction (IFMP for short) of 2D images, which is capable of not only predicting a series of coherent human body motions, but also reconstructing a detailed textured 3D human body model from only two image frames. The method automatically focuses on key parts of the image through action coding and uses a conditional generative adversarial network to generate a series of consecutive intermediate frame images. A depth-aware implicit function representation is combined to map a 3D model from the 2D image, and high quality textures of the human body in a clothed state are obtained by texture mapping and model detail enhancement methods. Finally, the experimental results validate the advantages of the IFMP method in image action coherence prediction, as well as verifying the effectiveness of the human 3D model generated by the method in terms of geometric accuracy and texture quality.

In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric structures and textures. To address these challenges, an implicit–explicit coupling enhancement for a UAV large-scale scene modeling framework is proposed. Benefiting from the mutual promotion of implicit and explicit models, we initially address the issue of missing co-visibility clusters caused by environmental noise through large-scale implicit modeling with UAVs. This enhances the inter-frame photometric and geometric consistency. Subsequently, we enhance the multi-view point cloud reconstruction density via synthetic co-visibility clusters, effectively recovering missing spatial information and constructing a more complete dense point cloud. Finally, during the mesh modeling phase, high-quality 3D modeling of large-scale UAV scenes is achieved by inversely radiating and mapping additional texture details into 3D voxels. The experimental results demonstrate that our method achieves state-of-the-art modeling accuracy across various scenarios, outperforming existing commercial UAV aerial photography software (COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6) and related algorithms. [ABSTRACT FROM AUTHOR]

The incorporation of detailed textures in 3D city models is crucial for enhancing their realism, as it adds depth and authenticity to the visual representation, thereby closely mimicking the surfaces and materials found in actual urban environments. Existing 3D city models can be enriched with energy-related roof and façade details, such as the material type (such as windows, green façades, bricks) and sunlight reflectance which can be derived from texture information. However, a common limitation of these models is their lack of very high resolution textures, which which reduces their realism and detail. Manually mapping textures onto each surface of a building is an exceptionally time-consuming and labor-intensive process, making it unfeasible for large-scale applications involving thousands of buildings. Therefore, an automated method is essential for texture mapping of 3D city models from aerial imagery. In this paper, we present CityJSON texture mapper – a python-based software tool for automated texture mapping of CityJSON-based 3D city models from oblique and nadir aerial imagery. Experimental results demonstrate the effectiveness of our approach in generating high-quality textured 3D city models, showcasing the potential for broader applications in geospatial analysis and decision-making. This research contributes to the ongoing efforts in enhancing the realism and usability of CityJSON-based 3D city models by enhancing them with their real textures from oblique aerial imagery. Texture mapped model can be explored at https://bit.ly/textured3dbag. [ABSTRACT FROM AUTHOR]

The three-dimensional (3D) reconstruction of buildings using photogrammetric point clouds is important for many applications, ranging from digital city construction to urban energy consumption analysis. However, problems such as building complexity and point cloud flaws may lead to incorrect modeling, which will affect subsequent steps such as texture mapping. This paper introduces a pipeline for building surface reconstruction from photogrammetric point clouds, employing a hybrid method that combines connection evaluation and framework optimization. Firstly, the plane segmentation method divides building point clouds into several pieces, which is complemented by a proposed candidate plane generation method aimed at removing redundancies and merging similarities. Secondly, the improved connection evaluation method detects potential skeleton lines from different planes. Subsequently, a framework optimization method is introduced to select suitable undirected polygonal boundaries from planes, forming the basis for plane primitives. Finally, by triangulating all plane primitives and filling holes, a building surface polygonal model is generated. Experiments conducted on various building examples provide both qualitative and quantitative evidence that the proposed hybrid method outperforms many existing methods, including traditional methods and deep learning methods. Notably, the proposed method successfully reconstructs the main building structures and intricate details, which can be further used to generate textural models and semantic models. Experimental results validate that the proposed method can be used for the surface reconstruction from photogrammetric point clouds of planar buildings. [ABSTRACT FROM AUTHOR]