Hegde, SrinidhiKullman, KaurGrubb, ThomasLait, LeslieGuimond, StephenZwicker, Matthias2024-12-112024-12-112024-07-26https://doi.org/10.48550/arXiv.2407.19097http://hdl.handle.net/11603/37044Exploring scientific datasets with billions of samples in real-time visualization presents a challenge - balancing high-fidelity rendering with speed. This work introduces a novel renderer - Neural Accelerated Renderer (NAR), that uses the neural deferred rendering framework to visualize large-scale scientific point cloud data. NAR 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 train a neural network to learn the point cloud geometry from a high-performance multi-stream rasterizer and capture the desired postprocessing effects from a conventional high-quality renderer. We demonstrate the effectiveness of NAR by visualizing complex multidimensional Lagrangian flow fields and photometric scans of a large terrain and compare the renderings against the state-of-the-art high-quality renderers. Through extensive evaluation, we demonstrate that NAR 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, we show that NAR 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.14 pagesen-USThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.Public Domainhttps://creativecommons.org/publicdomain/mark/1.0/Computer Science - Machine LearningComputer Science - Human-Computer InteractionComputer Science - Computer Vision and Pattern RecognitionComputer Science - GraphicsText