Forests are crucial terrestrial ecosystems. To understand long-term forest community evolution driven by multiple environmental factors, we constructed a 3D forest stand spatiotemporal evolution framework featuring synchronous bidirectional coupling between terrain, hydrology, radiation, and vegetation. First, we simulated topographical evolution using a physics-based procedural erosion method and introduced a multi-layer soil moisture model and individual tree growth response mechanisms to reflect-topography interactions, thereby establishing a realistic environmental basis for forest stand evolution. Second, leveraging real environmental data, we simulated growth responses and biomass changes of forest stands under different precipitation and radiation conditions, elucidated response mechanisms of individual tree attributes to environmental changes, and achieved intuitive evolution of 3D forest stands. The framework advances beyond unidirectional environmental forcing models by integrating hydraulic erosion, soil moisture dynamics, and slope-aware radiation within a unified monthly timestep, enabling co-evolutionary simulation of forest stands under dynamic landscapes. Finally, the computer-based model incorporated natural disaster events such as fires and droughts, with real-time interaction and visualization capabilities, supporting immersive and responsive forest landscape simulation and detailed spatiotemporal evolution analysis, enabling assessment of the dynamic recovery processes of forest stands under multiple disturbance scenarios.

Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests, mutual occlusion between trees, the scanner’s field of view, and terrain changes make the point clouds captured by laser scanning sensors incomplete, further hindering downstream tasks. This study proposes a skeleton-embedded tree point cloud completion method, termed SK-TreePCN, which recovers complete individual tree point clouds from incomplete scanning data in the field. SK-TreePCN employs a transformer trained on simulated point clouds generated by a 3D radiative transfer model. Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and F1 Score. The generated point clouds display a more natural appearance and clearer branches. The accuracy of tree height and diameter at breast height extracted from the recovered point cloud achieved R2 values of 0.929 and 0.904, respectively. SK-TreePCN demonstrates applicability and robustness in recovering individual tree point clouds. It demonstrated great potential for TLS-based field measurements of trees, refining point cloud 3D reconstruction and tree information extraction and reducing field data collection labor while retaining satisfactory data quality.

Garment is an essential component of digital humans, and the accurate representation of dynamic simulation details and wrinkle characteristics is crucial for enhancing the realism of virtual scenes. However, this task remains significantly challenging in complex simulation scenarios. Therefore, we propose a novel garment simulation method based on Graph Neural Networks (GNNs), referred to as SAGS-GNN, which effectively simulates self-collision and cloth anisotropy. To tackle the self-collision problem, we present the repulsive loss term and the maximum depth loss term. These terms effectively simulate the interactions between the vertices of the cloth mesh by jointly constraining their positions, thereby facilitating the self-collision handling of garments. Furthermore, our approach utilizes the Neo-Hookean StVK method to achieve anisotropy in cloth, further reflecting the different wrinkle details of multiple materials during motion. In summary, our SAGS method effectively mitigates the issue of interpenetration among garments, facilitates the realization of anisotropic properties in a variety of fabric materials, and significantly enhances the visual realism of virtual apparel. We evaluate our method on various garment types and materials, demonstrating competitive qualitative and quantitative results.

High-quality green gardens can markedly enhance the quality of life and mental well-being of their users. However, health and lifestyle constraints make it difficult for people to enjoy urban gardens, and traditional methods struggle to offer the high-fidelity experiences they need. This study introduces a 3D scene reconstruction and rendering strategy based on implicit neural representation through the efficient and removable neural radiation fields model (NeRF-RE). Leveraging neural radiance fields (NeRF), the model incorporates a multi-resolution hash grid and proposal network to improve training efficiency and modeling accuracy, while integrating a segment-anything model to safeguard public privacy. Take the crabapple tree, extensively utilized in urban garden design across temperate regions of the Northern Hemisphere. A dataset comprising 660 images of crabapple trees exhibiting three distinct geometric forms is collected to assess the NeRF-RE model’s performance. The results demonstrated that the ‘harvest gold’ crabapple scene had the highest reconstruction accuracy, with PSNR, LPIPS and SSIM of 24.80 dB, 0.34 and 0.74, respectively. Compared to the Mip-NeRF 360 model, the NeRF-RE model not only showed an up to 21-fold increase in training efficiency for three types of crabapple trees, but also exhibited a less pronounced impact of dataset size on reconstruction accuracy. This study reconstructs real scenes with high fidelity using virtual reality technology. It not only facilitates people’s personal enjoyment of the beauty of natural gardens at home, but also makes certain contributions to the publicity and promotion of urban landscapes.

Three-dimensional (3D) virtual trees play a vital role in modern forestry research, enabling the visualization of forest structures and supporting diverse simulations, including radiation transfer, climate change impacts, and dynamic forest management. Current virtual tree modeling primarily relies on 3D point cloud reconstruction from field survey data, and this approach faces significant challenges in scalability and structural diversity representation, limiting its broader applications in ecological modeling of forests. To address these limitations, we propose Diff-Tree, a novel diffusion model-based framework for generating diverse and realistic tree point cloud with reduced dependence on real-world data. The framework incorporates an innovative tree realism-aware filtering mechanism to ensure the authenticity of generated data while maintaining structural diversity. We validated Diff-Tree using two distinct datasets: one comprising five tree species from different families and genera, and another containing five Eucalyptus species from the same genus, demonstrating the method’s versatility across varying taxonomic levels. Quantitative evaluation shows that Diff-Tree successfully generates realistic tree point cloud while effectively enhancing structural diversity, achieving average MMDCD and COVCD values of (0.41, 65.78) and (0.56, 47.09) for the two datasets, respectively. The proposed method not only significantly reduces data acquisition costs but also provides a flexible, data-driven approach for virtual forest generation that adapts to diverse research requirements, offering a more efficient and practical solution for forestry research and ecological modeling.

Forest ecosystems can change due to both human activities and climatic factors, particularly shifts in temperature, rain, and wind patterns. Topographic changes caused by rains and structural shifts induced by forest fires represent two primary disturbance events in forest environments. These disturbances are influenced by weather factors and exhibit complex effects on forest dynamics, characterized by regional, seasonal, and stochastic variations. Consequently, examining the interactions between weather patterns and forest evolution through computer graphics holds significant research value. Vegetation and terrain modeling are fundamental to generating realistic forest landscapes. We employ physically-based procedural erosion to simulate geomorphological erosion processes, while further exploring vegetation-terrain interactions to create high-resolution landscapes. Using data from real forest landscapes, we incorporate fire ignition points to simulate forest fire occurrence and spread by modeling wildfire combustion and heat transfer processes, which accurately capture fire dynamics. This enables the simulation of forest fire scenarios under various environmental conditions, allowing us to assess the combined impacts of rainfall and forest fires on forest landscapes. Additionally, the model ensures real-time interaction, supporting the creation of immersive and responsive landscape simulations.

BackgroundAccurate understanding of 3D medical images requires a background of specialized medical knowledge. There is a pressing need for easy-to-understand medical visualization tools to help patients accurately interpret 3D image data, especially given the large number of patients requiring such assistance.ObjectiveIn this paper, we explore the design considerations of a multimodal medical visualization tool for interpreting 3D medical images, which can help users to understand and recognize 3D medical image data.MethodsAn observational study and focus group interviews were conducted to explore how patients interact with physicians and the main problems they encounter when interpreting 3D medical images. Additionally, we conducted semi-structured expert interviews with physicians to investigate the common methods, techniques, and challenges involved in doctor-patient communication when interpreting 3D medical images. We also organized a participatory design workshop to discuss the patients’ design preferences for medical visualization tools.ResultsThe study identified three types of physician-patient interactions, eight specific behaviors, and seven main issues. It also summarized eight common methods and techniques to aid in understanding 3D medical images and highlighted five key findings regarding design preferences for medical visualization tools. Based on previous studies and our empirical research results, we propose seven design considerations for designing visual interfaces, interaction design plans, audios, infographics, and animation guides. The comprehensive summary of the weights for the above-mentioned design consideration was obtained. A comprehensive weighting of design consideration elements was calculated based on the Analytic Hierarchy Process. The results show that the design consideration factors (A primary factors) that have the relatively big weights are the interaction design (57.091%) and visual interface (25.352%), and the ones that have relatively small weights are the medical education and popularization (12.766%), and text presentation (4.791%). Additionally, we found that the weights of factors of the design considerations (B primary factors) are different in the web application, software and VR/AR platforms. Furthermore, we presented a case study of the design of a multimodal medical visualization tool applied in the medical context to help patients interpret 3D medical image data and improve doctor-patient communication skills.ConclusionThis study explores the benefits of combining multiple visualization methods for both doctors and patients. We also discuss the advantages and challenges of designing and using multimodal visualization tools in medical settings.

There are generally two ways to ignite wildfires, including natural fire sources represented by lightning strikes and artificial fire sources generated by human production and daily use, both of which have regional and seasonal characteristics. For three-dimensional forest fire research, it is not easy to achieve complex global spread behavior simulation while considering the internal physical reactions of vegetation combustion. The study constructed different natural scenes based on different vegetation cells, described the principle of lightning ignition of combustibles, analyzed the spread results of wildfires under the influence of multiple weather factors in different scenes, and achieved repeatable wildfire research. At the same time, the virtual scene intuitively expresses the real fire extinguishing methods, providing relevant references for the design of fire extinguishing schemes. Compared to directly using physical models, this article uses a single wood pyrolysis model to couple vegetation’s morphological structure and physical reactions. By considering the spread of different vegetation types and the influence of multiple factors on forest fire spread, it expresses the complete forest fire behavior from ignition to extinction, significantly improving the realism and immersion of forest fires.

In recent years, the increasing frequency of forest fires has threatened ecological and social security. Due to the risks of traditional fire drills, three-dimensional visualization technology has been adopted to simulate forest fire management. This paper presents an immersive decision-making framework for forest firefighting, designed to simulate the response of resources during fires. First, a fire resource scheduling optimization model for multiple fire stations is proposed. This model integrates the characteristics of fire spread with a mixed-integer linear programming (MILP) framework, aiming to minimize response time and firefighting costs. It enables flexible resource scheduling optimization under various fire spread scenarios and constraints on firefighting resources. Second, the ant lion optimization algorithm (ALO) is enhanced, incorporating multiple firefighting weighting factors such as the density, distance, and wind direction of burning trees. This improvement allows for the dynamic selection of priority firefighting targets, facilitating the precise allocation of resources to efficiently complete fire suppression tasks. Finally, a three-dimensional virtual forest environment is developed to simulate real-time actions and processes during firefighting operations. The proposed framework provides an immersive and visualized real-time fire simulation method, offering valuable support for decision-making in forest fire management.

Urban street trees bring the beautiful ecological environment for human beings, but also may harm human health. Tree pollen is an important allergen that causes people to suffer from asthma and rhinitis, causing a serious medical burden. In order to protect human health and reduce medical costs, urban street trees need to be accurately identified. However, the identification of the urban street tree is influenced by the natural image (images with light intensity, season and shooting conditions), tree characteristics and identification models. To solve the problem, we proposed an interpretation model for identifying tree from natural images, named Ev2S_SHAP. Then, we applied the method to explain the influence of environmental and leaf factors on tree recognition and the identification accuracy. The open-source natural image dataset of urban street tree with complex situations such as different angles, distances, light, and times was used as the research object to verify the proposed tree identification model. The results showed that the overall evaluation of the identification results for 50 tree species: recall, precision, accuracy, and F1 score were 98.12 %, 98.18 %, 98.11 %, and 98.13 %, respectively. The identification accuracy of deciduous shrubs, evergreen shrubs, deciduous trees, and evergreen trees was 98.85 %, 98.47 %, 98.76 %, and 96.38 %, respectively. Complex lights and angles, long-range shooting, and winter conditions weakened the extraction of leaf features and were not conducive to the identification of tree species. Leaf shape characteristics had important influence on tree species identification. The effects of circularity, minimum circumcircle, and perimeter on tree identification accounted for 94.36 %, 75.61 %, and 69.12 %, respectively. Circularity was positively correlated to the identification contribution of elliptic leaf species, but opposite to that of lanceolate leaf species. The perimeter contributed positively correlated to the identification of lanceolate leaf species, but the minimum circumcircle was negatively. The Ev2S-SHAP effectively improved the identification accuracy of tree species. The study provides an innovative method for identifying and interpreting tree species.

森林是生态环境系统的重要组成部分.随着气候变暖,恶劣气候气象条件造成全球森林火灾频繁发生,给国民经济和消防救援带来巨大挑战,森林火灾已成为全球主要的自然灾害.因此,森林场景可视化建模、3维场景仿真、林火模拟仿真、火场复现、预测和灾害评估成为林业虚拟仿真研究热点.本文对树木形态结构建模技术、森林场景大规模重建和实时渲染、森林场景可视化、林火模型和林火模拟仿真等前沿技术和算法进行综述.对相关的林木、植被的形态结构表达和真实感可视化建模方法进行归纳分类,并对不同可视化方法的算法优劣、复杂度、实时渲染效率和适用场景进行讨论.基于规则的林木建模方法和基于林分特征的真实场景重建方法对大规模森林场景重建技术进行分类,基于物理模型、经验模型和半经验模型对森林火灾的林火模型、单木林火、多木林火模拟和蔓延进行总结,对影响林火蔓延的不同环境气象因子(如地形地貌、湿度、可燃物等)和森林分布对林火发生、扩散和蔓延的影响进行分析,对不同算法的优劣进行对比、分析和讨论,对森林场景可视化和林火模拟仿真技术未来的发展方向、存在问题和挑战进行展望.本文为基于森林真实场景的森林火灾模拟仿真和数字孪生沉浸式互动模拟系统的构建提供了理论方法基础,该平台可以实现森林场景快速构建、不同火源林火模拟、火场蔓延模拟仿真以及不同气象影响条件的火场预测,可对森林火场救援指挥、火场灾害评估和火场复原提供可视化决策支持.

To enhance the realism of garment and human body collision in real-time fabric simulation， this paper proposed an automated human body fitting collision method based on bounding volume and mesh. Firstly， according to the human skeletal structure and garment type， we optimize the skeletal information involved in collision simulation. It is convenient to obtain the feature points and semantically segment the human body. Secondly， the geometric shape of the human body is approximated with capsule colliders and mesh colliders according to the characteristics of skinning animation， which can follow the movement of the model. Lastly， the capsule colliders can realize rough collision detection rapidly and eliminate mesh colliders that are unlikely to detect. It achieves accurate collision based on the mesh collision method and reduces penetration. We use sphere colliders at joints with large deformation. The experiment demonstrates our approach can generate colliders automatically. It improves the fidelity of garment collision simulation and guarantees the efficiency of 3D garment simulation in real-time.

Many clinical works require medical inter-modality imaging results since the supplementary imaging information from different modalities can be combined to provide better decision-making. Traditionally, this is done by scanning patients with different modalities, which is expensive, time-consuming, laborious, and may have health risks. Motivated by this problem, we propose a GAN-based method called W-VCT2VMRIGAN, which can automatically synthesize volumetric MRI from volumetric CT despite the presence of approximately %6 of imperfectly-paired slices, and thus can reduce cost, time, labor, and health risks caused by the traditional method. To show its effectiveness, we applied brain and pelvis datasets from clinical works to it. We also qualitatively and quantitatively compared it with the state-of-the-art techniques. The experimental result shows that in reference to the ground truth, our method outperforms the state-of-the-art Pix2Pix (12%, 15%, 260% better in average SSIM, average MS-SSIM3, MOS for brain; 12%, 9%, 230% better in average SSIM, average MS-SSIM3, MOS for pelvis), CycleGAN (30%, 24%, 520% better in average SSIM, average MS-SSIM3, MOS for brain; 42%, 56%, 680% better in average SSIM, average MS-SSIM3, MOS for pelvis), and MedSynthesisV1 (2%, 1%, 380% better in average SSIM, average MS-SSIM3, MOS for brain; 10%, 9%, 150% better in average SSIM, average MS-SSIM3, MOS for pelvis) techniques. Furthermore, we performed an ablation study for our method. The experimental result shows that in comparison to other variants, our method is optimal. Finally, we performed an experiment to choose the optimal hyperparameter regarding the number of epochs. The experimental result shows that the optimal number of epochs for brain and pelvis datasets are 900 and 400, respectively.

Combustibles, topography, and weather factors are the three essential factors affecting forest fire behavior, and current forest fire spread models need to consider weather factors fully. This paper proposes a forest fire spread method based on environmental weather factors to present a visualized simulation of forest fire spread in the natural environment. Forest pyrolysis differs based on water content, so a single-tree pyrolysis model with temperature as its core has been constructed to describe the differences in forest pyrolysis during different seasons visually. In addition, based on the improved Huygens principle as the theoretical basis for forest fire spread, weather factors such as wind speed, wind direction, and precipitation controlled by weather are coupled with the forest fire spread process. And the forest fire spread in three-dimensional scenarios is simulated by considering environmental factors. The visualization of the forest fire extinguishing process caused by precipitation is realized. Finally, the interaction between rain and snow, terrain and trees is realized when precipitation affects the corresponding landscape and vegetation texture to enhance the realism of the constructed forest environment. In short, this paper proposes a forest fire spread method based on environmental weather factors, which intuitively expresses the influence of different weather factors on forest fire spread, thereby improving the immersive experience of the related senses and realizing realistic scene roaming.

There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

State-of-the-art approaches to forest fire spread are based on either 2D numerical simulations of trees on GIS or rough 3D visualization. In this paper, we approximate the tree form by dynamically changing sets of tree-shape modules according to the morphological structure and wind fields. Guided by finite state machine, we define the states of equilibrium, heating, pyrolysis, cooling and destruction of tree-shape modules. Interactions between tree-shape modules drive the state transfer to achieve forest fire spread. Additionally, Loose Quadtrees are adopted to the spatial distribution of trees, which allows us to maintain the visual fidelity of the representation while rendering the forest fire spreads in real-time. Our method allows us to construct the Jiufeng forest example about 10km x 10km extent at interactive rates. The capabilities of tree-shape modules and forest fire spread visualization are demonstrated by numerous examples.

As an important part of national natural resources, forest has important research value. The construction of virtual forest realistic model has economic and social benefits such as film and television special effects, game entertainment and forest ecology, and is conducive to the accumulation and arrangement of world digital forest resources. At present, different virtual forest modeling methods still have some deficiencies in expressing the reality of the model. Based on the analysis of the research status of virtual forest, this paper gives different classifications of realistic modeling methods, and summarizes the theoretical basis, application fields, advantages and disadvantages of reconstruction methods based on real world data, interactive modeling methods and system modeling methods based on rules or procedures. In addition, the most popular modeling software with strong applicability is summarized and compared. Finally, the existing problems and further development trend of realistic forest modeling methods are discussed.

为了提高模型识别效率,本文提出基于兴趣区域的多层特征融合的花卉图像分类方法,并有效应用于梅花细粒度图像分类.通过提取兴趣区域和多层特征融合强化图像特征,使用全局平均池化层替代Flatten层.采用联合均匀分布的交叉熵损失函数,提升了分类准确率.实验结果表明:此方法在标准数据集Oxford Flowers 102的分类准确率为93.57％,在梅花数据集Plum Flowers 17的分类准确率为85.47％.此方法通过融合多层图像特征,能够消除背景噪声的干扰,具有通用性.

Many studies focus on only one aspect while placing objects in virtual reality environment, such as efficiency, accuracy or interactivity. However, striking a balance between these aspects and taking into account multiple indicators is important as it is the key to improving user experience. Therefore, this paper proposes an efficient and interactive object placement method for recognizing controller trajectory in virtual reality environment. For creating user-friendly feedback, we visualize the intersection of the ray and the scene by linking the controller motion information and the ray. The trajectory is abstracted as point-clouds for matching, and the corresponding object is instantiated at the center of the trajectory. To verify the interactive performance and user satisfaction with this method, we carry out a study on user experience. The results show that both the efficiency and interaction interest are improved by applying our new method, which provides a good idea for the interactive design of virtual reality layout applications.

Tree modeling has been a widely-discussed topic in computer graphics. However, with existing methods, the modeling process is occupied with complex data collection and tedious parameter adjustment, lacking a rich sensory modeling experience. In this paper, we propose an approach to sketch-based tree modeling in an immersive virtual reality environment, aiming to lower the difficulty of modeling and enhance the immersion in the designing process. We first present a sketch sampling and points optimization algorithm to obtain the skeleton of the branch in 3D space. As generating geometry along the skeleton, we apply a vector-projection method to fix the branch polygon twisting. Then, we introduce a bidirectional ray-hit algorithm to determine the branch radius as real-time sketching. To generate random twigs on branches, we introduce a twigs generation algorithm based on Perlin noise and the parent branch direction. Finally, we design a series of interactive methods for users to create tree models in a 3D virtual scene with the VR HMD and controller. Experimental results indicate that our approach can accomplish creating realistic tree models in real-time. The interactive and immersive modeling experience enables users to readily convey their ideas on tree structures in a simple and direct way of sketching. Visualizing and modifying the real-time generated branch results can contribute to provoking the inspiration for creation. (C) 2020 Elsevier Ltd. All rights reserved.