Computer Graphics Forum最新文献_第2页

Procedural Multiscale Geometry Modeling using Implicit Surfaces 程序多尺度几何建模使用隐式曲面

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70233

Bojja Venu, Adam Bosak, Juan Raúl Padrón-Griffe

{"title":"Procedural Multiscale Geometry Modeling using Implicit Surfaces","authors":"Bojja Venu, Adam Bosak, Juan Raúl Padrón-Griffe","doi":"10.1111/cgf.70233","DOIUrl":"https://doi.org/10.1111/cgf.70233","url":null,"abstract":"Materials exhibit geometric structures across mesoscopic to microscopic scales, influencing macroscale properties such as appearance, mechanical strength, and thermal behavior. Capturing and modeling these multiscale structures is challenging but essential for computer graphics, engineering, and materials science. We present a framework inspired by hypertexture methods, using implicit surfaces and sphere tracing to synthesize multiscale structures on the fly without precomputation. This framework models volumetric materials with particulate, fibrous, porous, and laminar structures, allowing control over size, shape, density, distribution, and orientation. We enhance structural diversity by superimposing implicit periodic functions while improving computational efficiency. The framework also supports spatially varying particulate media, particle agglomeration, and piling on convex and concave structures, such as rock formations (mesoscale), without explicit simulation. We demonstrate its potential in the appearance modeling of volumetric materials and investigate how spatially varying properties affect the perceived macroscale appearance. As a proof of concept, we show that microstructures created by our framework can be reconstructed from image and distance values defined by implicit surfaces, using both first-order and gradient-free optimization methods.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometric Integration for Neural Control Variates 神经控制变量的几何积分

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70275

D. Meister, T. Harada

引用次数: 0

WaterGS: Physically-Based Imaging in Gaussian Splatting for Underwater Scene Reconstruction 基于物理成像的高斯溅射水下场景重建

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70270

S. Q. Wang, W. B. Wu, M. Shi, Z. X. Li, Q. Wang, D. M. Zhu

{"title":"WaterGS: Physically-Based Imaging in Gaussian Splatting for Underwater Scene Reconstruction","authors":"S. Q. Wang, W. B. Wu, M. Shi, Z. X. Li, Q. Wang, D. M. Zhu","doi":"10.1111/cgf.70270","DOIUrl":"https://doi.org/10.1111/cgf.70270","url":null,"abstract":"Reconstructing underwater object geometry from multi-view images is a long-standing challenge in computer graphics, primarily due to image degradation caused by underwater scattering, blur, and color shift. These degradations severely impair feature extraction and multi-view consistency. Existing methods typically rely on pre-trained image enhancement models as a preprocessing step, but often struggle with robustness under varying water conditions. To overcome these limitations, we propose WaterGS, a novel framework for underwater surface reconstruction that jointly recovers accurate 3D geometry and restores true object colors. The core of our approach lies in introducing a Physically-Based imaging model into the rendering process of 2D Gaussian Splatting. This enables accurate separation of true object colors from water-induced distortions, thereby facilitating more robust photometric alignment and denser geometric reconstruction across views. Building upon this improved photometric consistency, we further introduce a Gaussian bundle adjustment scheme guided by our physical model to jointly optimize camera poses and geometry, enhancing reconstruction accuracy. Extensive experiments on synthetic and real-world datasets show that WaterGS achieves robust, high-fidelity reconstruction directly from raw underwater images, outperforming prior approaches in both geometric accuracy and visual consistency.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LTC-IR: Multiview Edge-Aware Inverse Rendering with Linearly Transformed Cosines LTC-IR：基于线性余弦变换的多视图边缘感知逆渲染

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70262

Dabeen Park, Junsuh Park, Jooeun Son, Seungyoung Lee, Joo-Ho Lee

引用次数: 0

Accelerating Signed Distance Functions 加速符号距离函数

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70258

Pierre Hubert-Brierre, Eric Guérin, Adrien Peytavie, Eric Galin

{"title":"Accelerating Signed Distance Functions","authors":"Pierre Hubert-Brierre, Eric Guérin, Adrien Peytavie, Eric Galin","doi":"10.1111/cgf.70258","DOIUrl":"https://doi.org/10.1111/cgf.70258","url":null,"abstract":"Processing and particularly visualizing implicit surfaces remains computationally intensive when dealing with complex objects built from construction trees. We introduce optimization nodes to reduce the computational cost of the field function evaluation for hierarchical construction trees, while preserving the Lipschitz or conservative properties of the function. Our goal is to propose acceleration nodes directly embedded in the construction tree, and avoid external, accompanying data-structures such as octrees. We present proxy and continuous level of detail nodes to reduce the overall evaluation cost, along with a normal warping technique that enhances surface details with negligible computational overhead. Our approach is compatible with existing algorithms that aim at reducing the number of function calls. We validate our methods by computing timings as well as the average cost for traversing the tree and evaluating the signed distance field at a given point in space. Our method speeds-up signed distance field evaluation by up to three orders or magnitude, and applies both to ray-surface intersection computation in Sphere Tracing applications, and to polygonization algorithms.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Text-Guided Diffusion with Spectral Convolution for 3D Human Pose Estimation 文本引导扩散与光谱卷积三维人体姿态估计

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70263

Liyuan Shi, Suping Wu, Sheng Yang, Weibin Qiu, Dong Qiang, Jiarui Zhao

{"title":"Text-Guided Diffusion with Spectral Convolution for 3D Human Pose Estimation","authors":"Liyuan Shi, Suping Wu, Sheng Yang, Weibin Qiu, Dong Qiang, Jiarui Zhao","doi":"10.1111/cgf.70263","DOIUrl":"https://doi.org/10.1111/cgf.70263","url":null,"abstract":"Although significant progress has been made in monocular video-based 3D human pose estimation, existing methods lack guidance from fine-grained high-level prior knowledge such as action semantics and camera viewpoints, leading to significant challenges for pose reconstruction accuracy under scenarios with severely missing visual features, i.e., complex occlusion situations. We identify that the 3D human pose estimation task fundamentally constitutes a canonical inverse problem, and propose a motion-semantics-based diffusion(MS-Diff) framework to address this issue by incorporating high-level motion semantics with spectral feature regularization to eliminate interference noise in complex scenes and improve estimation accuracy. Specifically, we design a Multimodal Diffusion Interaction (MDI) module that incorporates motion semantics including action categories and camera viewpoints into the diffusion process, establishing semantic-visual feature alignment through a cross-modal mechanism to resolve pose ambiguities and effectively handle occlusions. Additionally, we leverage a Spectral Convolutional Regularization (SCR) module that implements adaptive filtering in the frequency domain to selectively suppress noise components. Extensive experiments on large-scale public datasets Human3.6M and MPI-INF-3DHP demonstrate that our method achieves state-of-the-art performance.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MF-SDF: Neural Implicit Surface Reconstruction using Mixed Incident Illumination and Fourier Feature Optimization MF-SDF：基于混合入射照明和傅里叶特征优化的神经隐式曲面重建

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70244

Xueyang Zhou, Xukun Shen, Yong Hu

{"title":"MF-SDF: Neural Implicit Surface Reconstruction using Mixed Incident Illumination and Fourier Feature Optimization","authors":"Xueyang Zhou, Xukun Shen, Yong Hu","doi":"10.1111/cgf.70244","DOIUrl":"https://doi.org/10.1111/cgf.70244","url":null,"abstract":"The utilization of neural implicit surface as a geometry representation has proven to be an effective multi-view surface reconstruction method. Despite the promising results achieved, reconstructing geometry from objects in real-world scenes remains challenging due to the interaction between surface materials and complex ambient light, as well as shadow effects caused by self-occlusion, making it a highly ill-posed problem. To address this challenge, we propose MF-SDF, a method that use a hybrid neural network and spherical gaussian representation to model environmental lighting, so that the model can express the situation of multiple light sources including directional light (such as outdoor sunlight) in real-world scenarios. Benefit from this, our method effectively reconstructs coherent surfaces and accurately locates the shadow location on the surface. Furthermore, we adopt a shadow aware multi-view photometric consistency loss, which mitigates the erroneous reconstruction results of previous methods on surfaces containing shadows, thereby improve the overall smoothness of the surface. Additionally, unlike previous approaches that directly optimize spatial features, we propose a Fourier feature optimization method that directly optimizes the tensorial feature in the frequency domain. By optimizing the high-frequency components, this approach further enhances the details of surface reconstruction. Finally, through experiments, we demonstrate that our method outperforms existing methods in terms of reconstruction accuracy on real captured data.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Swept Volume Computation with Enhanced Geometric Detail Preservation 增强几何细节保存的扫描体积计算

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70238

Pengfei Wang, Yuexin Yang, Shuangmin Chen, Shiqing Xin, Changhe Tu, Wenping Wang

引用次数: 0

SPG: Style-Prompting Guidance for Style-Specific Content Creation SPG：特定样式内容创建的样式提示指南

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70251

Qian Liang, Zichong Chen, Yang Zhou, Hui Huang

引用次数: 0

Gaussians on their Way: Wasserstein-Constrained 4D Gaussian Splatting with State-Space Modeling 高斯在他们的道路上：wasserstein约束的四维高斯溅射与状态空间建模

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70271

J. Deng, P. Shi, Y. Luo, Q. Li

{"title":"Gaussians on their Way: Wasserstein-Constrained 4D Gaussian Splatting with State-Space Modeling","authors":"J. Deng, P. Shi, Y. Luo, Q. Li","doi":"10.1111/cgf.70271","DOIUrl":"https://doi.org/10.1111/cgf.70271","url":null,"abstract":"Dynamic scene rendering has taken a leap forward with the rise of 4D Gaussian Splatting, but there is still one elusive challenge: how to make 3D Gaussians move through time as naturally as they would in the real world, all while keeping the motion smooth and consistent. In this paper, we present an approach that blends state-space modeling with Wasserstein geometry, enabling a more fluid and coherent representation of dynamic scenes. We introduce a State Consistency Filter that merges prior predictions with the current observations, enabling Gaussians to maintain coherent trajectories over time. We also employ Wasserstein Consistency Constraint to ensure smooth, consistent updates of Gaussian parameters, reducing motion artifacts. Lastly, we leverage Wasserstein geometry to capture both translational motion and shape deformations, creating a more geometrically consistent model for dynamic scenes. Our approach models the evolution of Gaussians along geodesics on the manifold of Gaussian distributions, achieving smoother, more realistic motion and stronger temporal coherence. Experimental results show consistent improvements in rendering quality and efficiency.(see https://www.acm.org/publications/class-2012)","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0