NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization

• URL: http://arxiv.org/abs/2407.19097v1
• Date: Fri, 26 Jul 2024 21:21:13 GMT
• Title: NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization
• Authors: Srinidhi Hegde, Kaur Kullman, Thomas Grubb, Leslie Lait, Stephen Guimond, Matthias Zwicker,
• Abstract summary: This work introduces a novel - Neural Accelerated Renderer (NAR) NAR uses the neural deferred rendering framework to visualize large-scale scientific point cloud data. We achieve competitive frame rates of $>$ 126 fps for interactive rendering of 350M points.
• Score: 15.7907024889244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Exploring 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 $\sim$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.

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