Pub Date : 2025-08-14DOI: 10.1016/j.gmod.2025.101294
Ran Chen , Xiang Xu , KaiYao Ge , Yanning Xu , Xiangxu Meng , Lu Wang
Soft shadows play a crucial role in enhancing visual realism in real-time rendering. Although traditional shadow mapping techniques offer high efficiency, they often suffer from artifacts and limited quality. In contrast, ray tracing can produce high-fidelity soft shadows but incurs substantial computational cost. In this paper, we propose a general-purpose, real-time soft shadow generation method based on neural networks. To encode shadow geometry, we employ the hard shadows via shadow mapping as input to our network, which effectively captures the spatial layout of shadow positions and contours. A lightweight U-Net architecture then refines this input to synthesize high-quality soft shadows in real time. The generated shadows closely approximate ray-traced references in visual fidelity. Compared to existing learning-based methods, our approach produces higher-quality soft shadows and offers improved generalization across diverse scenes. Furthermore, it requires no scene-specific precomputation, making it directly applicable to practical real-time rendering scenarios.
{"title":"Real-time neural soft shadow synthesis from hard shadows","authors":"Ran Chen , Xiang Xu , KaiYao Ge , Yanning Xu , Xiangxu Meng , Lu Wang","doi":"10.1016/j.gmod.2025.101294","DOIUrl":"10.1016/j.gmod.2025.101294","url":null,"abstract":"<div><div>Soft shadows play a crucial role in enhancing visual realism in real-time rendering. Although traditional shadow mapping techniques offer high efficiency, they often suffer from artifacts and limited quality. In contrast, ray tracing can produce high-fidelity soft shadows but incurs substantial computational cost. In this paper, we propose a general-purpose, real-time soft shadow generation method based on neural networks. To encode shadow geometry, we employ the hard shadows via shadow mapping as input to our network, which effectively captures the spatial layout of shadow positions and contours. A lightweight U-Net architecture then refines this input to synthesize high-quality soft shadows in real time. The generated shadows closely approximate ray-traced references in visual fidelity. Compared to existing learning-based methods, our approach produces higher-quality soft shadows and offers improved generalization across diverse scenes. Furthermore, it requires no scene-specific precomputation, making it directly applicable to practical real-time rendering scenarios.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101294"},"PeriodicalIF":2.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842156","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}
Pub Date : 2025-08-13DOI: 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.
{"title":"Feature line extraction based on winding number","authors":"Shuxian Cai , Juan Cao , Bailin Deng , Zhonggui Chen","doi":"10.1016/j.gmod.2025.101296","DOIUrl":"10.1016/j.gmod.2025.101296","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101296"},"PeriodicalIF":2.2,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829844","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}
Pub Date : 2025-08-13DOI: 10.1016/j.gmod.2025.101295
Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen
Vector graphics are widely used in areas such as logo design and digital painting, including both stroked and filled paths as primitives. GPU-based rendering for filled paths already has well-established solutions. Due to the complexity of stroked paths, existing methods often render them by approximating strokes with filled shapes. However, the performance of existing methods still leaves room for improvement. This paper designs a GPU-accelerated rendering algorithm along with a curvature-guided parallel adaptive subdivision method to accurately and efficiently render stroke areas. Additionally, we propose an efficient Newton iteration-based method for arc-length parameterization of quadratic curves, along with an error estimation technique. This enables a parallel rendering approach for dashed stroke styles and arc-length guided texture filling. Experimental results show that our method achieves average speedups of for rendering quadratic stroked paths and for rendering quadratic dashed strokes, compared to the best existing approaches.
{"title":"GPU-accelerated rendering of vector strokes with piecewise quadratic approximation","authors":"Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen","doi":"10.1016/j.gmod.2025.101295","DOIUrl":"10.1016/j.gmod.2025.101295","url":null,"abstract":"<div><div>Vector graphics are widely used in areas such as logo design and digital painting, including both stroked and filled paths as primitives. GPU-based rendering for filled paths already has well-established solutions. Due to the complexity of stroked paths, existing methods often render them by approximating strokes with filled shapes. However, the performance of existing methods still leaves room for improvement. This paper designs a GPU-accelerated rendering algorithm along with a curvature-guided parallel adaptive subdivision method to accurately and efficiently render stroke areas. Additionally, we propose an efficient Newton iteration-based method for arc-length parameterization of quadratic curves, along with an error estimation technique. This enables a parallel rendering approach for dashed stroke styles and arc-length guided texture filling. Experimental results show that our method achieves average speedups of <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>×</mo></mrow></math></span> for rendering quadratic stroked paths and <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> for rendering quadratic dashed strokes, compared to the best existing approaches.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101295"},"PeriodicalIF":2.2,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829843","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}
Pub Date : 2025-08-11DOI: 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.
{"title":"Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation","authors":"Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan","doi":"10.1016/j.gmod.2025.101290","DOIUrl":"10.1016/j.gmod.2025.101290","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101290"},"PeriodicalIF":2.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810321","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}
Pub Date : 2025-08-05DOI: 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.
{"title":"Position-free multiple-scattering computations for micrograin BSDF model","authors":"Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi","doi":"10.1016/j.gmod.2025.101288","DOIUrl":"10.1016/j.gmod.2025.101288","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101288"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771657","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}
Revolved 3D objects are widely used in industrial, manufacturing, and artistic fields, with subtractive manufacturing being a common production method. A key preprocessing step is to maximize raw material utilization by generating as many rough-machined inputs as possible from a single stock piece, which poses a packing problem constrained by tool accessibility. The main challenge is integrating tool accessibility into packing. This paper introduces the carvable packing problem for revolved objects, a critical but under-researched area in subtractive manufacturing. We propose a new carvable coarsening hull and a packing strategy that uses beam search and a bottom-up placement method to position these hulls in the stock material. Our method was tested on diverse sets of revolved objects with different geometries, and physical tests were conducted on a 5-axis machining platform, proving its ability to enhance material use and manufacturability.
{"title":"Carvable packing of revolved 3D objects for subtractive manufacturing","authors":"Chengdong Wei, Shuai Feng, Hao Xu, Qidong Zhang, Songyang Zhang, Zongzhen Li, Changhe Tu, Haisen Zhao","doi":"10.1016/j.gmod.2025.101282","DOIUrl":"10.1016/j.gmod.2025.101282","url":null,"abstract":"<div><div>Revolved 3D objects are widely used in industrial, manufacturing, and artistic fields, with subtractive manufacturing being a common production method. A key preprocessing step is to maximize raw material utilization by generating as many rough-machined inputs as possible from a single stock piece, which poses a packing problem constrained by tool accessibility. The main challenge is integrating tool accessibility into packing. This paper introduces the carvable packing problem for revolved objects, a critical but under-researched area in subtractive manufacturing. We propose a new carvable coarsening hull and a packing strategy that uses beam search and a bottom-up placement method to position these hulls in the stock material. Our method was tested on diverse sets of revolved objects with different geometries, and physical tests were conducted on a 5-axis machining platform, proving its ability to enhance material use and manufacturability.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101282"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779312","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}
Pub Date : 2025-08-05DOI: 10.1016/j.gmod.2025.101285
Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu
Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft’s effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines.
{"title":"TerraCraft: City-scale generative procedural modeling with natural languages","authors":"Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu","doi":"10.1016/j.gmod.2025.101285","DOIUrl":"10.1016/j.gmod.2025.101285","url":null,"abstract":"<div><div>Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft’s effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101285"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771658","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}
Pub Date : 2025-07-22DOI: 10.1016/j.gmod.2025.101281
Giovanni Pintore , Marco Agus , Alberto Signoroni , Enrico Gobbetti
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.
{"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":"2025-07-22","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}
Pub Date : 2025-07-11DOI: 10.1016/j.gmod.2025.101280
Tak Yu Lau , Dong He , Yamin Li , Yihe Wang , Danjie Bi , Lulu Huang , Pengcheng Hu , Kai Tang
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.
{"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":"2025-07-11","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}
Pub Date : 2025-07-03DOI: 10.1016/j.gmod.2025.101278
Xiaowei Li , Yingjie Wu , Yaohui Sun , Xin Chen , Yanru Chen , Yi-jun Yang
To improve the area-preserving parameterization quality of rational Bézier surfaces, an optimization algorithm using bilinear reparameterization is proposed. First, the rational Bézier surface is transformed using a rational bilinear transformation, which provides greater degrees of freedom compared to Möbius transformations, while preserving the rational Bézier representation. Then, the energy function is discretized using the composite Simpson’s rule, and its gradients are computed for optimization. Finally, the optimal rational bilinear transformation is determined using the L-BFGS method. Experimental results are presented to demonstrate the reparameterization effects through the circle-packing texture map, iso-parametric curve net, and color-coded images of APP energy in the proposed approach.
{"title":"Improving the area-preserving parameterization of rational Bézier surfaces by rational bilinear transformation","authors":"Xiaowei Li , Yingjie Wu , Yaohui Sun , Xin Chen , Yanru Chen , Yi-jun Yang","doi":"10.1016/j.gmod.2025.101278","DOIUrl":"10.1016/j.gmod.2025.101278","url":null,"abstract":"<div><div>To improve the area-preserving parameterization quality of rational Bézier surfaces, an optimization algorithm using bilinear reparameterization is proposed. First, the rational Bézier surface is transformed using a rational bilinear transformation, which provides greater degrees of freedom compared to Möbius transformations, while preserving the rational Bézier representation. Then, the energy function is discretized using the composite Simpson’s rule, and its gradients are computed for optimization. Finally, the optimal rational bilinear transformation is determined using the L-BFGS method. Experimental results are presented to demonstrate the reparameterization effects through the circle-packing texture map, iso-parametric curve net, and color-coded images of APP energy in the proposed approach.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101278"},"PeriodicalIF":2.5,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534220","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}
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