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(CA) Dynamic GPU Voxel Grids and Voxel-sequential Processing in NanoVDB

NanoVDB 的进步实现了动态 GPU 模拟和几何体操作

研究人员为 NanoVDB 数据结构引入了重大进展,实现了 GPU 上的动态拓扑更新。这些更新,包括形态学膨胀和网格合并,可以在毫秒内重建包含数亿个体素的网格。此外,还为 GPU 内核开发了一种新的体素序列访问范例,该范例最大限度地减少了树遍历,并允许复杂算子接近峰值内存带宽。这些改进消除了 NanoVDB 先前只读的限制,使其适用于动态 GPU 模拟和几何体操作,并在 3D 重建和流体模拟等应用中进行了评估。 AI

影响 实现了更高效、更动态的基于 GPU 的模拟和几何体操作,可能影响机器学习和科学计算等领域。

排序理由 详细介绍新数据结构进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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NanoVDB 的进步实现了动态 GPU 模拟和几何体操作

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详细介绍新数据结构进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 (CA) · Eftychios Sifakis, Francis Williams, Christopher Horvath, Rohan Sawhney, Mark Harris, Jonathan Swartz, Ken Museth ·

    NanoVDB 中的动态 GPU 体素网格和体素序列处理

    arXiv:2611.08665v1 Announce Type: cross Abstract: We introduce two fundamental advances to the NanoVDB data structure: dynamic GPU-native topological updates, and a voxel-sequential access paradigm that dramatically accelerates point-wise and stencil-based operators on sparse gri…