论文标题
通过多分辨率哈希编码基于神经表示的交互式体积可视化
Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation
论文作者
论文摘要
神经网络在压缩体积数据以进行可视化方面显示出很大的潜力。但是，由于训练和推断的高成本，迄今为止，这种体积神经表示仅应用于离线数据处理和非交互式渲染。在本文中，我们证明，通过同时利用现代的GPU张量核，一个天然的CUDA神经网络框架以及具有宏观细胞加速的精心设计的渲染算法，我们可以进行交互性地进行射线痕量痕量体积量化神经表示（10-60fps）。我们的神经表示也是高保真性（PSNR> 30dB）和紧凑型（小10-1000倍）。此外，我们表明可以在渲染环内部安装整个训练步骤，并完全跳过预训练过程。为了支持极端规模的数据数据，我们还制定了一种有效的核心外培训策略，这使我们的体积神经表示训练可以仅使用NVIDIA RTX 3090工作站来扩展到Terascale。
Neural networks have shown great potential in compressing volume data for visualization. However, due to the high cost of training and inference, such volumetric neural representations have thus far only been applied to offline data processing and non-interactive rendering. In this paper, we demonstrate that by simultaneously leveraging modern GPU tensor cores, a native CUDA neural network framework, and a well-designed rendering algorithm with macro-cell acceleration, we can interactively ray trace volumetric neural representations (10-60fps). Our neural representations are also high-fidelity (PSNR > 30dB) and compact (10-1000x smaller). Additionally, we show that it is possible to fit the entire training step inside a rendering loop and skip the pre-training process completely. To support extreme-scale volume data, we also develop an efficient out-of-core training strategy, which allows our volumetric neural representation training to potentially scale up to terascale using only an NVIDIA RTX 3090 workstation.
