发表机构
Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Pierce将复杂三维网格的空间连接转化为光线追踪操作，利用GPU硬件RT核心加速查询，在数字病理数据上比最先进方法快两个数量级。
AI 中文摘要
许多新兴应用，从计算生物学到数字孪生和城市规划，严重依赖多面体网格上的三维空间连接。这些连接包含计算密集型的三角形-三角形相交测试，该测试成对比较多面体网格的面。由于每个网格可能包含数千个面，由此产生的成本挑战了空间数据管理技术的响应性。现有技术遵循过滤-细化范式，要么通过索引加速过滤步骤，要么通过渐进式网格压缩结合三角形-三角形测试的GPU并行化加速细化步骤。然而，前者忽视了几何内部细化的高成本，而后者降低了这一成本但仍依赖于相同的成对三角形-三角形测试。在本文中，我们介绍了Pierce，一种将复杂多面体网格上的三维空间连接重新表述为光线追踪操作并利用现代GPU的硬件光线追踪单元（RT核心）来加速查询执行的方法。我们的方法沿着一个网格的边向另一个网格上构建的空间层次结构投射光线，在内部层级执行光线-节点测试以剪除远距离几何体，在叶节点执行光线-三角形测试以识别相交的网格，这两者均由RT核心在硬件中加速。我们在真实和合成数据上对Pierce进行了评估，与多个基线相比，在数字病理学数据上相较于最先进方法实现了超过两个数量级的加速。
英文摘要
Many emerging applications, from computational biology to digital twins and urban planning, rely heavily on three-dimensional spatial joins over polyhedral meshes. These joins comprise computationally-intensive triangle--triangle intersection tests that pairwise compare the faces of polyhedral meshes. Since each mesh may contain thousands of faces, the resulting cost challenges the responsiveness of spatial data management techniques. Existing techniques follow the filter-and-refine paradigm, accelerating either the filtering step through indexing or the refinement step through progressive mesh compression combined with GPU parallelization of triangle--triangle tests. However, the former neglects the high cost of intra-geometry refinements, whereas the latter lowers this cost but still relies on the same pairwise triangle--triangle tests. In this paper, we introduce Pierce, an approach that reformulates three-dimensional spatial joins over complex polyhedral meshes as ray-tracing operations and leverages the hardware ray-tracing units (RT cores) of modern GPUs to accelerate query execution. Our approach casts rays along the edges of one mesh against a spatial hierarchy built over the other, performing ray--node tests at the internal levels to prune distant geometries and ray--triangle tests at the leaves to identify intersecting meshes, both of which RT cores accelerate in hardware. We evaluated Pierce on real and synthetic data against multiple baselines, demonstrating more than two orders of magnitude speedup on digital pathology data compared to the state-of-the-art approach.
CommentsAccepted at ACM SIGSPATIAL 2026
