引用本文:王 祎,李文辉,张振花.一种基于分离包围盒的快速碰撞检测算法.软件学报,2008,19(zk):143-150
【打印本页】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 4532次   下载 8291 本文二维码信息
码上扫一扫!
分享到: 微信 更多
一种基于分离包围盒的快速碰撞检测算法
王 祎1, 李文辉1, 张振花1
吉林大学 计算机学院 符号计算与知识工程教育部重点实验室,吉林 长春 130012
摘要:
提出了一种基于分离包围盒(SBVs)的快速碰撞检测方法.SBVs的空间形态和位置由两个模型的最优分离平面所决定,这使得它不仅可以快速检测出分离模型,而且在模型相交的情况下能够有效地缩小精确检测的范围.为了能够快速计算SBVs,设计并验证了一种基于SVM的近似计算SBVs方法.最后将SBV和图形硬件的计算优势结合起来,以实现复杂模型相交区的穿刺查询.实验结果表明,基于SBVs的碰撞检测算法能够高效、平衡地处理无拓扑模型的分离、碰撞,尤其是穿刺等复杂情况.
关键词:  碰撞检测  包围盒  分离平面  支撑向量机
DOI:
分类号:
基金项目:Supported by the National Natural Science Foundation of China under Grant No.60573182 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2008AA10Z224 (国家高技术研究发展计划(863)); the Doctoral Fund of the Ministry of Education of China under Grant No.20060183042 (国家教育部博士点基金); the Jilin Province Sci-Tech Development Plan of China under Grant No.20060527 (吉林省科技发展计划)
Efficient Collision Detection Algorithm Based on Separating Bounding Volumes
WANG Yi,LI Wen-Hui,ZHANG Zhen-Hua
Abstract:
An efficient collision detection method based on separating bounding volume (SBV) is proposed. The positions and shapes of SBVs are determined by the optimal separating support hyper planes of two objects. SBVs not only can efficiently detect the separation of models, but have a high culling ratio when models are intersecting. In order to compute SBVs efficiently, an approximate method using SVM is also put forward and tested. At last in penetration region, a method combined with GPU and SBVs is designed to handle the proximity queries. Experimental results illustrate that SBVs based collision detection algorithm is applicable to exact collision detection for 3D models even without topologies and achieves more efficient and balanced performances in separating, colliding and especially puncturing cases.
Key words:  collision detection  bounding volumes  separating plane  support vector machines

引用本文:
【打印本页】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载  
分享到: 微信 更多
摘要:
关键词:  
DOI:
分类号:
基金项目:
Abstract:
Key words: