Graphical Models最新文献_第4页

Sparse support path generation for multi-axis curved layer fused filament fabrication 多轴弯曲层熔丝制造的稀疏支撑路径生成

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-07-11 DOI: 10.1016/j.gmod.2025.101280

Tak Yu Lau , Dong He , Yamin Li , Yihe Wang , Danjie Bi , Lulu Huang , Pengcheng Hu , Kai Tang

{"title":"Sparse support path generation for multi-axis curved layer fused filament fabrication","authors":"Tak Yu Lau , Dong He , Yamin Li , Yihe Wang , Danjie Bi , Lulu Huang , Pengcheng Hu , Kai Tang","doi":"10.1016/j.gmod.2025.101280","DOIUrl":"10.1016/j.gmod.2025.101280","url":null,"abstract":"<div><div>In recent years, multi-axis fused filament fabrication has emerged as a solution to address the limitations of the conventional 2.5D printing process. By using a curved layering strategy and varying the print direction, the final parts can be printed with reduced support structures, enhanced surface quality, and improved mechanical properties. However, support structures in the multi-axis scheme are still needed sometimes when the support-free requirement conflicts with other constraints. Currently, most support generation algorithms are for the conventional 2.5D printing, which are not applicable to multi-axis printing. To address this issue, we propose a sparse and curved support filling pattern for multi-axis printing, aiming at enhancing the material efficiency by fully utilizing the bridge technique. Firstly, the overhang regions are detected by identifying the overhang points given a multi-axis nozzle path. Then, an optimization framework for the support guide curve is proposed to minimize its total length while ensuring that overhang filaments can be stably supported. Lastly, the support layer slices and support segments that satisfy the self-supported criterion are generated for the final support printing paths. Simulation and experiments have been performed to validate the proposed methodology.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101280"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595734","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

VolumeDiffusion: Feed-forward text-to-3D generation with efficient volumetric encoder VolumeDiffusion：前馈文本到3d生成与高效的体积编码器

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-06-18 DOI: 10.1016/j.gmod.2025.101274

Zhicong Tang , Shuyang Gu , Chunyu Wang , Ting Zhang , Jianmin Bao , Dong Chen , Baining Guo

{"title":"VolumeDiffusion: Feed-forward text-to-3D generation with efficient volumetric encoder","authors":"Zhicong Tang , Shuyang Gu , Chunyu Wang , Ting Zhang , Jianmin Bao , Dong Chen , Baining Guo","doi":"10.1016/j.gmod.2025.101274","DOIUrl":"10.1016/j.gmod.2025.101274","url":null,"abstract":"<div><div>This work presents VolumeDiffusion, a novel feed-forward text-to-3D generation framework that directly synthesizes 3D objects from textual descriptions. It bypasses the conventional score distillation loss based or text-to-image-to-3D approaches. To scale up the training data for the diffusion model, a novel 3D volumetric encoder is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101274"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314598","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

Improving the area-preserving parameterization of rational Bézier surfaces by rational bilinear transformation 利用有理双线性变换改进有理bsamzier曲面的保面积参数化

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-07-03 DOI: 10.1016/j.gmod.2025.101278

Xiaowei Li , Yingjie Wu , Yaohui Sun , Xin Chen , Yanru Chen , Yi-jun Yang

引用次数: 0

Point cloud geometry compression based on the combination of interlayer residual and IRN concatenated residual 基于层间残差与IRN级联残差结合的点云几何压缩

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-06-27 DOI: 10.1016/j.gmod.2025.101279

Meng Huang, Qian Xu, Wenxuan Xu

{"title":"Point cloud geometry compression based on the combination of interlayer residual and IRN concatenated residual","authors":"Meng Huang, Qian Xu, Wenxuan Xu","doi":"10.1016/j.gmod.2025.101279","DOIUrl":"10.1016/j.gmod.2025.101279","url":null,"abstract":"<div><div>Point clouds have been attracting more and more attentions due to its capability of representing objects precisely, such as autonomous vehicle navigation, VR/AR, cultural heritage protection, etc. However, the enormous amount of data carried in point clouds presents significant challenges for transmission and storage. To solve this problem, this dissertation presents a point cloud compression framework based on the combination of interlayer residual and IRN concatenated residual. This paper deployed upsampling design after downsampled point cloud data. It calculates the residuals among point cloud data through downsampling and upsampling processes, consequently, maintains accuracy and reduces errors within the downsampling process. In addition, a novel Inception ResNet-Concatenated Residual Module is designed for maintaining the spatial correlation between layers and blocks. At the same time, it can extract the global and detailed features within point cloud data. Besides, Attention Module is dedicated to enhance the focus on salient features. Respectively compared with the traditional (G-PCC) and the learning point cloud compression method (PCGC v2), this paper lists a series of solid experiments data proving a 70% to 90% and a 6% to 9% BD-Rate gains on 8iVFB and Owlii datasets.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101279"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490700","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

RS-SpecSDF: Reflection-supervised surface reconstruction and material estimation for specular indoor scenes RS-SpecSDF：镜面室内场景的反射监督表面重建和材料估计

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-06-25 DOI: 10.1016/j.gmod.2025.101277

Dong-Yu Chen, Hao-Xiang Chen, Qun-Ce Xu, Tai-Jiang Mu

{"title":"RS-SpecSDF: Reflection-supervised surface reconstruction and material estimation for specular indoor scenes","authors":"Dong-Yu Chen, Hao-Xiang Chen, Qun-Ce Xu, Tai-Jiang Mu","doi":"10.1016/j.gmod.2025.101277","DOIUrl":"10.1016/j.gmod.2025.101277","url":null,"abstract":"<div><div>Neural Radiance Field (NeRF) has achieved impressive 3D reconstruction quality using implicit scene representations. However, planar specular reflections pose significant challenges in the 3D reconstruction task. It is a common practice to decompose the scene into physically real geometries and virtual images produced by the reflections. However, current methods struggle to resolve the ambiguities in the decomposition process, because they mostly rely on mirror masks as external cues. They also fail to acquire accurate surface materials, which is essential for downstream applications of the recovered geometries. In this paper, we present RS-SpecSDF, a novel framework for indoor scene surface reconstruction that can faithfully reconstruct specular reflectors while accurately decomposing the reflection from the scene geometries and recovering the accurate specular fraction and diffuse appearance of the surface without requiring mirror masks. Our key idea is to perform reflection ray-casting and use it as supervision for the decomposition of reflection and surface material. Our method is based on an observation that the virtual image seen by the camera ray should be consistent with the object that the ray hits after reflecting off the specular surface. To leverage this constraint, we propose the Reflection Consistency Loss and Reflection Certainty Loss to regularize the decomposition. Experiments conducted on both our newly-proposed synthetic dataset and a real-captured dataset demonstrate that our method achieves high-quality surface reconstruction and accurate material decomposition results without the need of mirror masks.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101277"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472382","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

L2-GNN: Graph neural networks with fast spectral filters using twice linear parameterization L2-GNN：使用两次线性参数化的快速光谱滤波的图神经网络

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-06-26 DOI: 10.1016/j.gmod.2025.101276

Siying Huang , Xin Yang , Zhengda Lu , Hongxing Qin , Huaiwen Zhang , Yiqun Wang

{"title":"L2-GNN: Graph neural networks with fast spectral filters using twice linear parameterization","authors":"Siying Huang , Xin Yang , Zhengda Lu , Hongxing Qin , Huaiwen Zhang , Yiqun Wang","doi":"10.1016/j.gmod.2025.101276","DOIUrl":"10.1016/j.gmod.2025.101276","url":null,"abstract":"<div><div>To improve learning on irregular 3D shapes, such as meshes with varying discretizations and point clouds with different samplings, we propose L<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-GNN, a new graph neural network that approximates the spectral filters using twice linear parameterization. First, we parameterize the spectral filters using wavelet filter basis functions. The parameterization allows for an enlarged receptive field of graph convolutions, which can simultaneously capture low-frequency and high-frequency information. Second, we parameterize the wavelet filter basis functions using Chebyshev polynomial basis functions. This parameterization reduces the computational complexity of graph convolutions while maintaining robustness to the change of mesh discretization and point cloud sampling. Our L<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>-GNN based on the fast spectral filter can be used for shape correspondence, classification, and segmentation tasks on non-regular mesh or point cloud data. Experimental results show that our method outperforms the current state of the art in terms of both quality and efficiency.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101276"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490699","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

LDM: Large tensorial SDF model for textured mesh generation LDM：用于纹理网格生成的大张量SDF模型

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-06-21 DOI: 10.1016/j.gmod.2025.101271

Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo

{"title":"LDM: Large tensorial SDF model for textured mesh generation","authors":"Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo","doi":"10.1016/j.gmod.2025.101271","DOIUrl":"10.1016/j.gmod.2025.101271","url":null,"abstract":"<div><div>Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required by modern rendering pipelines. In this paper, we propose LDM, a <strong>L</strong>arge tensorial S<strong>D</strong>F <strong>M</strong>odel, which introduces a novel feed-forward framework capable of generating high-fidelity, illumination-decoupled textured mesh from a single image or text prompts. We firstly utilize a multi-view diffusion model to generate sparse multi-view inputs from single images or text prompts, and then a transformer-based model is trained to predict a tensorial SDF field from these sparse multi-view image inputs. Finally, we employ a gradient-based mesh optimization layer to refine this model, enabling it to produce an SDF field from which high-quality textured meshes can be extracted. Extensive experiments demonstrate that our method can generate diverse, high-quality 3D mesh assets with corresponding decomposed RGB textures within seconds. The project code is available at <span><span>https://github.com/rgxie/LDM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101271"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330266","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

DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments dddd++：利用密度图一致性在室内环境中进行深度估计

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-08-01 Epub Date: 2025-07-22 DOI: 10.1016/j.gmod.2025.101281

Giovanni Pintore , Marco Agus , Alberto Signoroni , Enrico Gobbetti

{"title":"DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments","authors":"Giovanni Pintore , Marco Agus , Alberto Signoroni , Enrico Gobbetti","doi":"10.1016/j.gmod.2025.101281","DOIUrl":"10.1016/j.gmod.2025.101281","url":null,"abstract":"<div><div>We introduce a novel deep neural network designed for fast and structurally consistent monocular 360° depth estimation in indoor settings. Our model generates a spherical depth map from a single gravity-aligned or gravity-rectified equirectangular image, ensuring the predicted depth aligns with the typical depth distribution and structural features of cluttered indoor spaces, which are generally enclosed by walls, floors, and ceilings. By leveraging the distinctive vertical and horizontal patterns found in man-made indoor environments, we propose a streamlined network architecture that incorporates gravity-aligned feature flattening and specialized vision transformers. Through flattening, these transformers fully exploit the omnidirectional nature of the input without requiring patch segmentation or positional encoding. To further enhance structural consistency, we introduce a novel loss function that assesses density map consistency by projecting points from the predicted depth map onto a horizontal plane and a cylindrical proxy. This lightweight architecture requires fewer tunable parameters and computational resources than competing methods. Our comparative evaluation shows that our approach improves depth estimation accuracy while ensuring greater structural consistency compared to existing methods. For these reasons, it promises to be suitable for incorporation in real-time solutions, as well as a building block in more complex structural analysis and segmentation methods.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101281"},"PeriodicalIF":2.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679962","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

DC-APIC: A decomposed compatible affine particle in cell transfer scheme for non-sticky solid–fluid interactions in MPM DC-APIC：在MPM中非粘性固-液相互作用的细胞转移方案中分解的兼容仿射粒子

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-01 Epub Date: 2025-05-25 DOI: 10.1016/j.gmod.2025.101269

Chenhui Wang , Jianyang Zhang , Chen Li , Changbo Wang

{"title":"DC-APIC: A decomposed compatible affine particle in cell transfer scheme for non-sticky solid–fluid interactions in MPM","authors":"Chenhui Wang , Jianyang Zhang , Chen Li , Changbo Wang","doi":"10.1016/j.gmod.2025.101269","DOIUrl":"10.1016/j.gmod.2025.101269","url":null,"abstract":"<div><div>Despite the material point method (MPM) provides a unified particle simulation framework for coupling of different materials, MPM suffers from sticky numerical artifacts, which is inherently restricted to sticky and no-slip interactions. In this paper, we propose a novel transfer scheme called Decomposed Compatible Affine Particle in Cell (DC-APIC) within the MPM framework for simulating the two-way coupled interaction between elastic solids and incompressible fluids under free-slip boundary conditions on a unified background grid. Firstly, we adopt particle-grid compatibility to describe the relationship between grid nodes and particles at the fluid–solid interface, which serves as the guideline for subsequent particle–grid–particle transfers. Then we develop a phase-field gradient method to track the compatibility and normal directions at the interface. Secondly, to facilitate automatic MPM collision resolution during solid–fluid coupling, in the proposed DC-APIC integrator, the tangential component will not be transferred between incompatible grid nodes to prevent velocity smoothing in another phase, while the normal component is transferred without limitations. Finally, our comprehensive results confirm that our approach effectively reduces diffusion and unphysical viscosity compared to traditional MPM.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101269"},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134591","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

Semantics-aware human motion generation from audio instructions 从音频指令生成语义感知的人体运动

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-01 Epub Date: 2025-05-23 DOI: 10.1016/j.gmod.2025.101268

Zi-An Wang , Shihao Zou , Shiyao Yu , Mingyuan Zhang , Chao Dong

引用次数: 0