The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this l...The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.展开更多
Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computatio...Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computational cost during deformations, a dense model is often manipulated through a simplified structure, called cage, which envelops the model. However, cages are usually built interactively by users, which is tedious and time-consuming. In this paper, we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes. The method consists of two steps： 1） simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage; 2） removing the self-intersections of the coarse cage with Delaunay partitions. With this new method, a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies. Experimental results show that, compared to other cage building methods with the same number of vertex, cages built by our method are more similar to the input models. Thus, the dense models can be manipulated with higher accuracy through our cages.展开更多
在布料仿真过程中,基于位置的动力学方法(Position Based Dynamics, PBD)在布料模拟的效果及实时性方面展现了其优越的性能,但在布料网格精度提升时,为了加快其收敛速度而增加迭代计算次数,会导致模拟效率大幅降低及约束震荡现象的出现...在布料仿真过程中,基于位置的动力学方法(Position Based Dynamics, PBD)在布料模拟的效果及实时性方面展现了其优越的性能,但在布料网格精度提升时,为了加快其收敛速度而增加迭代计算次数,会导致模拟效率大幅降低及约束震荡现象的出现。为了解决以上问题,文章结合对位置动力学方法的研究,提出一种以多重网格思想为核心的布料模拟方法,采用基于二次误差度量(Quadric Error Metrics, QEM)的网格简化法对布料多重网格进行构建,同时结合空间插值算法进行层与层之间的数据传递及位置矫正,可以构建出质量良好的三角网格结构,避免不规则多边形的出现;在布料层次化模拟的基础上,提出结合逐次超松弛迭代法(Successive Over Relaxation, SOR)与Gauss-Seidel方法(GS)的混合平滑方案并验证了其有效性。经实验分析,布料层次化模拟方法提高了质点数目增加后的收敛速度,具有一定的稳定性。展开更多
基金Our research was funded by the Sichuan Key Provincial Research Base of Intelligent Tourism(No.ZHZJ23-02)supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A006)+1 种基金Additional support was provided by the National Cultural and Tourism Science and Technology Innovation Research andDevelopment Project(No.202417)the Lantern Culture and Crafts Innovation Key Laboratory Project of the Sichuan ProvincialDepartment of Culture and Tourism(No.SCWLCD-A02).
文摘The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.
基金Supported by the NSFC-Guangdong Joint Fund under Grant Nos. U0735001,U0835004,U0935004the National Basic Research 973 Program of China under Grant No. 2011CB302204
文摘Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computational cost during deformations, a dense model is often manipulated through a simplified structure, called cage, which envelops the model. However, cages are usually built interactively by users, which is tedious and time-consuming. In this paper, we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes. The method consists of two steps： 1） simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage; 2） removing the self-intersections of the coarse cage with Delaunay partitions. With this new method, a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies. Experimental results show that, compared to other cage building methods with the same number of vertex, cages built by our method are more similar to the input models. Thus, the dense models can be manipulated with higher accuracy through our cages.
文摘在布料仿真过程中,基于位置的动力学方法(Position Based Dynamics, PBD)在布料模拟的效果及实时性方面展现了其优越的性能,但在布料网格精度提升时,为了加快其收敛速度而增加迭代计算次数,会导致模拟效率大幅降低及约束震荡现象的出现。为了解决以上问题,文章结合对位置动力学方法的研究,提出一种以多重网格思想为核心的布料模拟方法,采用基于二次误差度量(Quadric Error Metrics, QEM)的网格简化法对布料多重网格进行构建,同时结合空间插值算法进行层与层之间的数据传递及位置矫正,可以构建出质量良好的三角网格结构,避免不规则多边形的出现;在布料层次化模拟的基础上,提出结合逐次超松弛迭代法(Successive Over Relaxation, SOR)与Gauss-Seidel方法(GS)的混合平滑方案并验证了其有效性。经实验分析,布料层次化模拟方法提高了质点数目增加后的收敛速度,具有一定的稳定性。
