NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization
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arXiv
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| Format: | Preprint |
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2024
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| author | Hegde, Srinidhi Kullman, Kaur Grubb, Thomas Lait, Leslie Guimond, Stephen Zwicker, Matthias |
| author_facet | Hegde, Srinidhi Kullman, Kaur Grubb, Thomas Lait, Leslie Guimond, Stephen Zwicker, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization Hegde, Srinidhi Kullman, Kaur Grubb, Thomas Lait, Leslie Guimond, Stephen Zwicker, Matthias Graphics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization |
| topic | Graphics Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2407.19097 |