Deep Neural Network-Based Cloth Collision Detection Algorithm

4区 计算机科学 Q3 Computer Science
Yanxia Jin, Zhiru Shi, Jing Yang, Yabian Liu, Xingyu Qiao, Ling Zhang
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引用次数: 0

Abstract

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations.
基于深度神经网络的布料碰撞检测算法
碰撞检测算法的质量直接影响整个仿真系统的性能。针对虚拟环境中柔性布碰撞检测效率低、准确率低的问题,本文提出了一种简化模型的定向边界框(OBB)算法、根节点双边界框的树形结构以及基于 OpenNN 神经网络优化的连续碰撞检测算法。首先,对于建模较为复杂的与布相互作用的物体,采用基于二次方误差度量的曲面简化算法对模型进行简化,并利用简化后的模型构建 OBB。其次,改进了碰撞检测中常用的边界框技术,并提出了根节点双边界框算法,以减少边界框的构建时间。最后,利用神经网络来优化连续碰撞检测算法,因为神经网络可以高效处理大量数据并移除不相关的碰撞对。实验表明,使用简化模型构建 OBB 与使用原始模型构建 OBB 几乎相同,但构建 OBB 所需的时间缩短了约 2.7 倍。对于相同的布,根节点双边界框算法比传统混合边界框算法节省 5.51%-11.32% 的时间。在平均去除率与传统过滤方法几乎相同的情况下,使用基于 OpenNN 神经网络优化的连续碰撞检测算法所需的时间减少了 7%-11%。仿真结果真实可靠,符合实时布料仿真的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Programming
Scientific Programming 工程技术-计算机:软件工程
自引率
0.00%
发文量
1059
审稿时长
>12 weeks
期刊介绍: Scientific Programming is a peer-reviewed, open access journal that provides a meeting ground for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing. The journal publishes papers on language, compiler, and programming environment issues for scientific computing. Of particular interest are contributions to programming and software engineering for grid computing, high performance computing, processing very large data sets, supercomputing, visualization, and parallel computing. All languages used in scientific programming as well as scientific programming libraries are within the scope of the journal.
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