ME-BVH:内存效率边界卷层次结构

E. Shellshear, Yi Li, J. Carlson
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引用次数: 0

摘要

碰撞检测和距离计算算法经常成为工业过程中许多数字人体建模仿真的瓶颈。在设计车辆装配线或协作机器人装配单元时,为了高效和安全的操作,必须能够准确地模拟无碰撞的相互作用。因此,任何改进这种算法的尝试都会产生广泛而重大的影响。然而,随着模型变得越来越大,场景变得越来越逼真,模拟包括更多元素,如肌肉骨骼模型和3D人体建模,邻近查询性能的其他部分变得越来越重要,如内存管理。在本文中,我们演示了一种名为ME-BVH(内存高效边界卷层次结构)的新技术,以改善具有边界卷层次结构的邻近查询的内存使用。该方法利用一种简单而有效的方法,在叶级上将原语分组在一起,并在分组的原语叶上自上而下地构建边界体层次结构。然后,本文展示了有效执行原始和边界卷查询的方法,以抵消大量潜在查询。此外,所做的修改非常简单,可以很容易地应用于大多数边界卷层次结构。通过使用这些方法,我们在具有数百万个原语的许多实际组装场景中演示了,与现有方法相比,我们提出的方法能够节省多达一半的内存使用,并且可以以很小的查询性能为代价减少构建时间。此外,与许多其他方法不同,这里开发的方法与人体工程学模拟中使用的所有BVH类型和查询兼容。所开发的算法通过减少构建边界体层次结构所需的时间,为数字人体建模中使用的可变形网格的接近查询提供了优势,由于网格变形,在模拟过程中通常必须多次重建或更新边界体层次结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ME-BVH: Memory Efficient Bounding Volume Hierarchies
Collision detection and distance computation algorithms often form the bottlenecks of many digital human modelling simulations in industrial processes. When designing vehicle assembly lines or cobot assembly cells it is essential to be able to accurately simulate collision free interactions both for efficient and safe operations. Hence, any attempt to improve such algorithms can have a broad and significant impact. Most of the focus is typically on speeding up the queries, however, with models becoming larger as scenarios become more realistic and simulations include more elements such as musculoskeletal models and 3D human body modelling, other parts of the proximity query performance are becoming important such as the management of memory. In this paper, we demonstrate a new technique called ME-BVH (Memory Efficient Bounding Volume Hierarchies) to improve memory usage for proximity queries with bounding volume hierarchies. The approach utilizes a simple and effective way of grouping primitives together at the leaf level and building the bounding volume hierarchy top down to the grouped primitive leaves. The paper then shows ways of efficiently carrying out primitive and bounding volume queries to offset the greater number of potential queries. In addition, the modifications taken are simple enough to be easily applied to most bounding volume hierarchies. By using these approaches, we demonstrate on a number of real-life assembly scenarios with millions of primitives that, compared to existing approaches, our proposed method is able to save up to half of the memory used and can reduce the build times at little cost to the query performance. In addition, the methods developed here are compatible with all BVH types and queries used in ergonomic simulations, unlike many other approaches. The developed algorithms present advantages for proximity queries for deformable meshes used in digital human modelling by reducing the time it takes to build a bounding volume hierarchy which often must be rebuilt or updated many times during simulations due to mesh deformations.
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