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.

翻译：神经网络在压缩体数据进行可视化方面展现出巨大潜力。然而，由于训练和推理的高昂成本，此类体神经表示迄今仅应用于离线数据处理和非交互式渲染。本文证明，通过同时利用现代GPU张量核心、原生CUDA神经网络框架以及结合宏单元加速的精巧渲染算法，我们能够实现体神经表示的交互式光线追踪（10-60帧/秒）。我们的神经表示同时具备高保真度（PSNR>30dB）与紧凑性（压缩10-1000倍）。此外，我们展示了将完整训练步骤嵌入渲染循环的可行性，从而完全跳过预训练过程。为支持超大规模体数据，我们还开发了高效的外存训练策略，该策略仅需使用NVIDIA RTX 3090工作站即可将体神经表示训练扩展至TB级规模。