Graphical Models最新文献

英文中文

Structural optimization of lattice structures using deep neural networks as geometry representation 使用深度神经网络作为几何表示的晶格结构优化

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.gmod.2025.101307

Michael Kofler, Michael Giritsch, Stefanie Elgeti

In this paper we present a lattice structure optimization approach by leveraging the capabilities of neural networks for implicit geometry representation. We employ the Deep Signed Distance Field (DeepSDF) method, where a continuous and low-dimensional latent space is introduced to encode the geometric information. In contrast to traditional topology optimization methods, this allows the restriction of the design space to specific geometries. In our case, the latent space is used to represent the geometry of different unit cells, that are stacked to form a lattice structure. Moreover, continuously varying the latent vector over the structure allows a functional grading and optimization. Unlike other lattice-structure optimization methods, we neither assume a large separation of scale nor periodicity. Instead, we perform a full-scale finite element analysis at each optimization step. The required mesh is obtained by a differentiable extension of the dual marching cubes algorithm, which enables gradient-based optimization.

在本文中，我们提出了一种利用神经网络的能力进行隐式几何表示的晶格结构优化方法。我们采用深度签名距离场（Deep Signed Distance Field, DeepSDF）方法，在该方法中引入一个连续的低维潜在空间来编码几何信息。与传统的拓扑优化方法相比，这种方法允许将设计空间限制为特定的几何形状。在我们的案例中，潜在空间用于表示不同单元格的几何形状，这些单元格堆叠形成晶格结构。此外，在结构上连续变化潜在向量允许功能分级和优化。与其他晶格结构优化方法不同，我们既没有假设大的尺度分离，也没有假设周期性。相反，我们在每个优化步骤中执行全面的有限元分析。通过对偶行进立方体算法的可微扩展获得所需的网格，从而实现基于梯度的优化。

引用次数: 0

FlexPlan: High-flexibility interactive floorplan design based on ArchiGraph FlexPlan：基于ArchiGraph的高灵活性交互式平面图设计

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.gmod.2025.101303

Zhan Wang , Junhao Wang , Zongpu Li , Hao Su , Pei Lv , Mingliang Xu

AI-aided floorplan design is a longstanding task in computer graphics. However, most of the existing methods focus on generating floorplans by limited architecture-level elements (e.g., room sizes, positions, and adjacencies), which ignore environmental factors and do not support customized designs. In this paper, we propose FlexPlan, an interactive approach for high-flexibility floorplan design. In FlexPlan, we propose a novel graph structure, named ArchiGraph, which enables flexible editing more comprehensive layout elements (e.g., architectures, environments, human needs) in a floorplan. First, we match similar floorplans according to the input architecture and environment features. Then, leveraging ArchiGraph, we interactively produce rooms’ attributes and quickly output the vectorized floorplans. For ArchiGraph, we design a RelationNet to predict room adjacencies, and propose a BoxNet to generate high-quality room boxes. Subjective and objective experiments show that our method is compatible with generating diverse complex floorplans (e.g., floorplans with irregular layout boundaries and room shapes). Compared with the state-of-the-art methods, our method can produce higher quality floorplans, and increase the speed of layout generation by nearly 20 times at most.

人工智能辅助平面图设计是计算机图形学中一项长期存在的任务。然而，大多数现有的方法侧重于通过有限的建筑级元素（例如，房间大小，位置和邻接关系）生成平面图，这忽略了环境因素，不支持定制设计。在本文中，我们提出了FlexPlan，一种用于高灵活性平面设计的交互式方法。在FlexPlan中，我们提出了一种新的图形结构，称为ArchiGraph，它可以在平面图中灵活地编辑更全面的布局元素（例如，建筑，环境，人的需求）。首先，我们根据输入的建筑和环境特征匹配相似的平面图。然后，利用ArchiGraph，我们交互地生成房间的属性，并快速输出矢量化的平面图。对于ArchiGraph，我们设计了一个RelationNet来预测房间邻接关系，并提出了一个BoxNet来生成高质量的房间盒子。主观和客观实验表明，我们的方法可以生成各种复杂的平面布置图（如不规则布局边界和房间形状的平面布置图）。与现有的方法相比，我们的方法可以生成更高质量的平面布置图，并将平面布置图生成速度最多提高近20倍。

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引用次数: 0

Feature line extraction based on winding number 基于圈数的特征线提取

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-13 DOI: 10.1016/j.gmod.2025.101296

Shuxian Cai , Juan Cao , Bailin Deng , Zhonggui Chen

Sharp feature lines provide critical structural information in 3D models and are essential for geometric processing. However, the performance of existing algorithms for extracting feature lines from point clouds remains sensitive to the quality of the input data. This paper introduces an algorithm specifically designed to extract feature lines from 3D point clouds. The algorithm calculates the winding number for each point and uses variations in this number within edge regions to identify feature points. These feature points are then mapped onto a cuboid structure to obtain key feature points and capture neighboring relationships. Finally, feature lines are fitted based on the connectivity of key feature points. Extensive experiments demonstrate that this algorithm not only accurately detects feature points on potential sharp edges, but also outperforms existing methods in extracting subtle feature lines and handling complex point clouds.

尖锐的特征线在三维模型中提供关键的结构信息，对几何处理至关重要。然而，现有的从点云中提取特征线的算法的性能对输入数据的质量仍然很敏感。本文介绍了一种从三维点云中提取特征线的算法。该算法计算每个点的圈数，并利用边缘区域内圈数的变化来识别特征点。然后将这些特征点映射到一个长方体结构上，以获得关键特征点并捕获相邻关系。最后，根据关键特征点的连通性拟合特征线。大量实验表明，该算法不仅能够准确地检测出潜在尖锐边缘上的特征点，而且在提取细微特征线和处理复杂点云方面都优于现有方法。

引用次数: 0

Collision-free path planning method for digital orthodontic treatment 指指正畸治疗的无碰撞路径规划方法

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-15 DOI: 10.1016/j.gmod.2025.101297

Hao Yu , Longdu Liu , Shuangmin Chen , Lin Lu , Yuanfeng Zhou , Shiqing Xin , Changhe Tu

The rapid evolution of digital orthodontics has highlighted a critical need for automated treatment planning systems that balance computational efficiency with clinical reliability. However, existing methods still suffer from several limitations, including excessive clinician involvement (accounting for over 35% of treatment planning time), reliance on empirically defined key frames, and limited biomechanical plausibility, particularly in cases of severe dental crowding. This paper proposes a novel collision-free optimization framework to address these issues simultaneously. Our method defines a total movement energy function evaluated over each tooth’s pose at intermediate time frames. This energy is minimized iteratively using a steepest descent strategy. A rollback mechanism is employed: if inter-tooth penetration is detected during an update, the step size is halved repeatedly until collisions are eliminated. The framework allows flexible control over the number of intermediate frames to enforce a strict constraint on per-tooth displacement, limiting it to 0.2 mm translation or

2 °

rotation every 10 to 14 days. Clinical evaluations show that the proposed algorithm can generate desirable and clinically valid tooth movement plans, even in complex cases, while significantly reducing the need for manual intervention.

数字正畸的快速发展突出了对平衡计算效率和临床可靠性的自动化治疗计划系统的迫切需求。然而，现有的方法仍然存在一些局限性，包括过多的临床医生参与（占治疗计划时间的35%以上），依赖经验定义的关键框架，以及有限的生物力学合理性，特别是在严重牙齿拥挤的情况下。本文提出了一种新的无碰撞优化框架来同时解决这些问题。我们的方法定义了在中间时间框架内评估每个牙齿姿势的总运动能量函数。使用最陡下降策略迭代最小化该能量。采用回滚机制：如果在更新期间检测到齿间渗透，则步长重复减半，直到消除碰撞。该框架允许灵活控制中间框架的数量，以严格限制每颗牙齿的位移，将其限制在每10至14天0.2毫米的平移或2°旋转。临床评估表明，即使在复杂的情况下，该算法也能产生理想的临床有效的牙齿移动计划，同时大大减少了人工干预的需要。

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引用次数: 0

Position-free multiple-scattering computations for micrograin BSDF model 微颗粒BSDF模型的无位置多次散射计算

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-05 DOI: 10.1016/j.gmod.2025.101288

Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi

Porous materials (e.g., weathered stone, industrial coatings) exhibit complex optical effects due to their micrograin and pore structures, posing challenges for photorealistic rendering. Explicit geometry models struggle to characterize their micrograin distributions at microscopic scales, while single-scattering microfacet model fails to accurately capture the multiple-scattering effects and causes energy non-conservation artifacts, manifesting as unrealistic luminance decay. We propose an enhanced micrograin BSDF model that accurately accounts for multiple scattering. First, we introduce a visible normal distribution function (VNDF) sampling method via rejection sampling. Building on VNDF sampling, we derive a position-free microsurface formulation incorporating both inter-micrograin and micrograin-to-base interactions. Furthermore, we propose a practical random walk method to simulate microsurface scattering, which accurately solves the derived formulation. Our micrograin BSDF model effectively eliminates the energy loss artifacts inherent in the previous model while significantly reducing noise, providing a physically accurate yet artistically controllable solution for rendering porous materials.

多孔材料（如风化石、工业涂料）由于其微颗粒和孔结构而表现出复杂的光学效果，这对真实感渲染提出了挑战。显式几何模型难以在微观尺度上表征其微观颗粒分布，而单散射微面模型无法准确捕捉多重散射效应，并导致能量非守恒伪像，表现为不切实际的亮度衰减。我们提出了一种增强的微颗粒BSDF模型，可以准确地解释多次散射。首先，我们通过拒绝抽样引入了一种可见正态分布函数（VNDF）抽样方法。在VNDF采样的基础上，我们推导了一个无位置的微表面配方，包括微颗粒间和微颗粒与基体的相互作用。此外，我们提出了一种实用的随机漫步方法来模拟微表面散射，该方法可以准确地求解推导出的公式。我们的微颗粒BSDF模型有效地消除了先前模型中固有的能量损失伪像，同时显着降低了噪声，为渲染多孔材料提供了物理上准确但艺术上可控的解决方案。

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引用次数: 0

Adaptive mesh-aligned Gaussian Splatting for monocular human avatar reconstruction 自适应网格对齐高斯飞溅单目人体头像重建

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-25 DOI: 10.1016/j.gmod.2025.101300

Hai Yuan , Xia Yuan , Yanli Liu , Guanyu Xing , Jing Hu , Xi Wu , Zijun Zhou

Virtual human avatars are essential for applications such as gaming, augmented reality, and virtual production. However, existing methods struggle to achieve high fidelity reconstruction from monocular input while keeping hardware costs low. Many approaches rely on the SMPL body prior and apply vertex offsets to represent clothed avatars. Unfortunately, excessive offsets often cause misalignment and blurred contours, particularly around clothing wrinkles, silhouette boundaries, and facial regions. To address these limitations, we propose a dual branch framework for human avatar reconstruction from monocular video. A lightweight Vertex Align Net (VAN) predicts per-vertex normal direction offsets on the SMPL mesh to achieve coarse geometric alignment and guide Gaussian-based human avatar modeling. In parallel, we construct a high resolution facial Gaussian branch based on FLAME estimated parameters, with facial regions localized via pretrained detectors. The facial and body renderings are fused using a semantic mask to enhance facial clarity and ensure globally consistent avatar appearance. Experiments demonstrate that our method surpasses state of the art approaches in modeling animatable human avatars with fine grained fidelity.

虚拟的人类化身对于游戏、增强现实和虚拟生产等应用是必不可少的。然而，现有的方法很难从单目输入实现高保真重建，同时保持低硬件成本。许多方法依赖于SMPL主体，并应用顶点偏移来表示穿着衣服的角色。不幸的是，过度的偏移往往会导致轮廓不一致和模糊，特别是在衣服褶皱、轮廓边界和面部区域。为了解决这些限制，我们提出了一个双分支框架，用于从单目视频中重建人类头像。一个轻量级的顶点对齐网络（VAN）预测SMPL网格上的每个顶点法线方向偏移，以实现粗几何对齐并指导基于高斯的人类化身建模。同时，我们基于FLAME估计参数构建了一个高分辨率的面部高斯分支，并通过预训练的检测器对面部区域进行了定位。面部和身体渲染融合使用语义掩码，以增强面部清晰度，并确保全球一致的化身外观。实验表明，我们的方法在建模具有细粒度保真度的可动画人类化身方面超越了最先进的方法。

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引用次数: 0

Disambiguating flat spots in discrete scalar fields 离散标量场中的平点消歧

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-27 DOI: 10.1016/j.gmod.2025.101299

L. Rocca , F. Iuricich , E. Puppo

We consider 2D scalar fields sampled on a regular grid. When the gradient is low relative to the resolution of the dataset’s range, the signal may contain flat spots: connected areas where all points share the same value. Flat spots hinder certain analyses, such as topological characterization or drainage network computations. We present an algorithm to determine a symbolic slope inside flat spots and consistently place a minimal set of critical points, in a way that is less biased than state-of-the-art methods. We present experimental results on both synthetic and real data, demonstrating how our method provides a more plausible positioning of critical points and a better recovery of the Morse–Smale complex.

我们考虑在规则网格上采样的二维标量场。当梯度相对于数据集范围的分辨率较低时，信号可能包含平坦点：所有点共享相同值的连接区域。平坦点阻碍了某些分析，如拓扑表征或排水网络计算。我们提出了一种算法来确定平面点内的符号斜率，并始终如一地放置最小临界点集，以一种比最先进的方法更少偏见的方式。我们给出了合成和真实数据的实验结果，证明了我们的方法如何提供更合理的临界点定位和更好的莫尔斯-斯莫尔复合物的恢复。

引用次数: 0

Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation 通过伪回放生成的场景理解领域增量学习范式

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-11 DOI: 10.1016/j.gmod.2025.101290

Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan

Scene understanding is a computer vision task that involves grasping the pixel-level distribution of objects. Unlike most research focuses on single-scene models, we consider a more versatile proposal: domain-incremental learning for scene understanding. This allows us to adapt well-studied single-scene models into multi-scene models, reducing data requirements and ensuring model flexibility. However, domain-incremental learning that leverages correlations between scene domains has yet to be explored. To address this challenge, we propose a Domain-Incremental Learning Paradigm (D-ILP) for scene understanding, along with a new strategy of Pseudo-Replay Generation (PRG) that does not require manual labeling. Specifically, D-ILP leverages pre-trained single-scene models and incremental images for supervised training to acquire new knowledge from other scenes. As a pre-trained generation model, PRG can controllably generate pseudo-replays resembling source images from incremental images and text prompts. These pseudo-replays are utilized to minimize catastrophic forgetting in the original scene. We perform experiments with three publicly accessible models: Mask2Former, Segformer, and DeepLabv3+. With successfully transforming these single-scene models into multi-scene models, we achieve high-quality parsing results for original and new scenes simultaneously. Meanwhile, the validity and rationality of our method are proved by the analysis of D-ILP.

场景理解是一项计算机视觉任务，涉及捕捉物体的像素级分布。与大多数专注于单场景模型的研究不同，我们考虑了一个更通用的建议：用于场景理解的领域增量学习。这使我们能够将经过充分研究的单场景模型适应为多场景模型，减少数据需求并确保模型灵活性。然而，利用场景域之间的相关性的领域增量学习尚未被探索。为了应对这一挑战，我们提出了一种用于场景理解的领域增量学习范式（D-ILP），以及一种不需要手动标记的伪回放生成（PRG）新策略。具体来说，D-ILP利用预训练的单场景模型和增量图像进行监督训练，从其他场景中获取新知识。作为一种预训练生成模型，PRG可以从增量图像和文本提示中可控地生成类似源图像的伪重播。这些伪回放被用来最小化原始场景中的灾难性遗忘。我们使用三个可公开访问的模型进行实验：Mask2Former， Segformer和DeepLabv3+。通过将这些单场景模型成功地转化为多场景模型，我们可以同时获得高质量的原始场景和新场景解析结果。同时，通过对D-ILP的分析，验证了该方法的有效性和合理性。

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引用次数: 0

DIFF: A dataset for indoor flexible furniture DIFF：室内柔性家具的数据集

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-19 DOI: 10.1016/j.gmod.2025.101293

Jia-Hong Liu , Shao-Kui Zhang , Shuran Sun , Zihao Wang , Song-Hai Zhang

Recently, indoor scene synthesis has gathered significant attention, leading to the development of numerous indoor datasets. However, existing datasets only address static furniture and scenes, ignoring the need for dynamic interior design scenarios that emphasize flexible functionalities. Addressing this gap, we present DIFF (Dataset for Indoor Flexible Furniture), featuring expertly crafted and labeled furniture modules capable of inter-transforming between different states, e.g., a cabinet can be inter-transformed to a desk. Each module exhibits flexibility in shifting to multiple shapes and functionalities. Additionally, we propose a method that adapts our dataset to generate flexible layouts. By matching our flexible objects to objects from existing datasets, we use a graph-based approach to migrate the spatial relation priors for optimizing a layout; subsequent layouts are then generated by minimizing a transition-cost function. Analyses and user studies validate the quality of our modules and demonstrate the plausibility of the proposed method.

近年来，室内场景合成引起了人们的极大关注，导致了大量室内数据集的开发。然而，现有的数据集只涉及静态家具和场景，忽略了强调灵活功能的动态室内设计场景的需求。为了解决这一差距，我们提出了DIFF（室内柔性家具数据集），其特点是能够在不同状态之间相互转换的精心制作和标记的家具模块，例如，一个橱柜可以相互转换为一张桌子。每个模块都可以灵活地转换为多种形状和功能。此外，我们提出了一种方法来适应我们的数据集，以产生灵活的布局。通过将我们的柔性对象与现有数据集中的对象进行匹配，我们使用基于图的方法来迁移空间关系先验以优化布局；然后通过最小化过渡成本函数生成后续布局。分析和用户研究验证了我们模块的质量，并证明了所提出方法的合理性。

引用次数: 0

DISCO: Efficient Diffusion Solver for large-scale Combinatorial Optimization problems 大规模组合优化问题的高效扩散求解器

IF 2.2 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-09-01 Epub Date: 2025-08-21 DOI: 10.1016/j.gmod.2025.101284

Hang Zhao , Kexiong Yu , Yuhang Huang , Renjiao Yi , Chenyang Zhu , Kai Xu

Combinatorial Optimization (CO) problems are fundamentally important in numerous real-world applications across diverse industries, notably computer graphics, characterized by entailing enormous solution space and demanding time-sensitive response. Despite recent advancements in neural solvers, their limited expressiveness struggles to capture the multi-modal nature of CO landscapes. While some research has adopted diffusion models, these methods sample solutions indiscriminately from the entire NP-complete solution space with time-consuming denoising processes, limiting scalability for large-scale problems. We propose DISCO, an efficient DIffusion Solver for large-scale Combinatorial Optimization problems that excels in both solution quality and inference speed. DISCO’s efficacy is twofold: First, it enhances solution quality by constraining the sampling space to a more meaningful domain guided by solution residues, while preserving the multi-modal properties of the output distributions. Second, it accelerates the denoising process through an analytically solvable approach, enabling solution sampling with very few reverse-time steps and significantly reducing inference time. This inference-speed advantage is further amplified by Jittor, a high-performance learning framework based on just-in-time compiling and meta-operators. DISCO delivers strong performance on large-scale Traveling Salesman Problems and challenging Maximal Independent Set benchmarks, with inference duration up to 5.38 times faster than existing diffusion solver alternatives. We apply DISCO to design 2D/3D TSP Art, enabling the generation of fluid stroke sequences at reduced path costs. By incorporating DISCO’s multi-modal property into a divide-and-conquer strategy, it can further generalize to solve unseen-scale instances out of the box.

组合优化（CO）问题在不同行业的许多实际应用中非常重要，特别是计算机图形学，其特点是需要巨大的解决方案空间和要求时间敏感的响应。尽管最近神经求解器取得了进步，但它们有限的表现力难以捕捉CO景观的多模态本质。虽然一些研究采用了扩散模型，但这些方法不加选择地从整个np完全解空间中采样解，并且耗时去噪，限制了大规模问题的可扩展性。我们提出DISCO，一个有效的大规模组合优化问题的扩散求解器，在解决质量和推理速度上都很出色。DISCO的效果是双重的：首先，它通过将采样空间约束到由解残数引导的更有意义的域来提高解的质量，同时保留了输出分布的多模态性质。其次，它通过解析可解的方法加速了去噪过程，使解采样具有很少的逆时间步长，并显着减少了推理时间。Jittor是一种基于即时编译和元操作符的高性能学习框架，它进一步增强了这种推断速度的优势。DISCO在大规模旅行推销员问题和具有挑战性的最大独立集基准上提供了强大的性能，推理持续时间比现有的扩散求解器替代品快5.38倍。我们将DISCO应用于设计2D/3D TSP Art，从而能够以更低的路径成本生成流体冲程序列。通过将DISCO的多模态特性结合到分而治之的策略中，它可以进一步推广到解决开箱即用的看不见的规模实例。

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