通过基于神经表示的多分辨率哈希编码进行交互式体积可视化
概括
这项研究引入了用于神经体积染的实时光线跟踪,显著压缩数据并实现交互式可视化. 这种新的方法绕过了预训练,并在单个GPU上扩展到 terascale 数据.
科学领域:
- 计算机图形 计算机图形
- 科学可视化科学可视化
- 机器学习 机器学习
背景情况:
- 隐式神经网络为体积可视化提供数据压缩.
- 高的培训和推断成本限制了当前神经网络应用程序的离线处理.
研究的目的:
- 为了实现体积神经表示的实时直接射线跟踪.
- 克服现有方法的局限性,以互动可视化大规模体积数据.
主要方法:
- 利用GPU张力芯,CUDA,优化体积染和加速数据结构.
- 实施极大规模数据的核心外培训策略.
- 在染循环中整合训练,绕过预训练.
主要成果:
- 实现了高保真度的神经表示 (PSNR>30dB),数据大小缩小至1000倍.
- 启用实时染和交互式可视化.
- 在单个工作站GPU上演示了 terascale 训练.
结论:
- 这种新的方法在训练时间,重建质量和染性能方面明显优于最先进的方法.
- 这种方法非常适合需要快速,准确地可视化大规模体积数据的应用.
- 实时神经体积染现在可以用于交互式应用程序.
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