Efficient collision detection using hybrid medial axis transform and BVH for rigid body simulation

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Graphical Models Pub Date : 2023-07-01 DOI:10.1016/j.gmod.2023.101180

Xingxin Li, Shibo Song, Junfeng Yao, Hanyin Zhang, Rongzhou Zhou, Qingqi Hong

引用次数: 0

Abstract

Medial Axis Transform (MAT) has been recently adopted as the acceleration structure of broad-phase collision detection. Compared to traditional BVH-based methods, MAT can provide a high-fidelity volumetric approximation of 3D complex objects, resulting in higher collision culling efficiency. However, due to MAT’s non-hierarchical structure, it may be outperformed in collision-light scenarios because several cullings at the top level of a BVH may take a large number of cullings with MAT. We propose a collision detection method that combines MAT and BVH to address the above problem. Our technique efficiently culls collisions between dynamic and static objects. Experimental results show that our method has higher culling efficiency than pure BVH or MAT methods.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于混合中轴变换和BVH的刚体仿真高效碰撞检测

中轴变换(MAT)作为宽相位碰撞检测的加速结构，近年来得到了广泛的应用。与传统的基于bvh的方法相比，MAT可以提供高保真的三维复杂物体的体积近似，从而提高碰撞剔除效率。然而，由于MAT的非分层结构，由于在BVH的顶层进行多次剔除可能需要使用MAT进行大量剔除，因此它在轻碰撞场景中的表现可能会更好。我们提出了一种结合MAT和BVH的碰撞检测方法来解决上述问题。我们的技术有效地剔除了动态和静态对象之间的碰撞。实验结果表明，该方法比纯BVH或MAT方法具有更高的剔除效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.