Exploring scientific datasets with billions of samples in real-time visualization presents a challenge - balancing high-fidelity rendering with speed. This work introduces a neural accelerated renderer, NARVis, that uses the neural deferred rendering framework to visualize large-scale scientific point cloud data. NARVis augments a real-time point cloud rendering pipeline with high-quality neural post-processing, making the approach ideal for interactive visualization at scale. Specifically, we render the multi-attribute point cloud using a high-performance multi-attribute rasterizer and train a neural renderer to capture the desired post-processing effects from a conventional high-quality renderer. NARVis is effective in visualizing complex multidimensional Lagrangian flow fields and photometric scans of a large terrain as compared to the state-of-the-art high-quality renderers. Extensive evaluations demonstrate that NARVis prioritizes speed and scalability while retaining high visual fidelity. We achieve competitive frame rates of $>$126 fps for interactive rendering of $>$350M points (i.e., an effective throughput of $>$44 billion points per second) using ~12 GB of memory on RTX 2080 Ti GPU. Furthermore, NARVis is generalizable across different point clouds with similar visualization needs and the desired post-processing effects could be obtained with substantial high quality even at lower resolutions of the original point cloud, further reducing the memory requirements.

翻译：在实时可视化中探索包含数十亿样本的科学数据集面临一项挑战——平衡高保真渲染与速度。本文提出了一种神经加速渲染器NARVis，利用神经延迟渲染框架实现大规模科学点云数据的可视化。NARVis通过高质量神经后处理增强实时点云渲染管线，使其特别适用于大规模交互式可视化场景。具体而言，我们使用高性能多属性光栅化器渲染多属性点云，并训练神经渲染器从传统高质量渲染器中捕获所需后处理效果。与现有最先进的高质量渲染器相比，NARVis在可视化复杂多维拉格朗日流场和大规模地形光度扫描方面表现优异。大量评估表明，NARVis在保持高视觉保真度的同时优先考虑速度与可扩展性。我们在RTX 2080 Ti GPU上使用约12 GB内存实现了对超过3.5亿个点的交互式渲染，帧率可达126 fps以上（即有效吞吐量超过每秒440亿个点）。此外，NARVis对具有相似可视化需求的不同点云具有泛化能力，即使在原始点云较低分辨率下也能获得高质量的后处理效果，进一步降低了内存需求。