Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916950163062784 |
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| author | Han, Mengjiao Sewell, Andres Insley, Joseph Knowles, Janet Mateevitsi, Victor A. Papka, Michael E. Petruzza, Steve Rizzi, Silvio |
| author_facet | Han, Mengjiao Sewell, Andres Insley, Joseph Knowles, Janet Mateevitsi, Victor A. Papka, Michael E. Petruzza, Steve Rizzi, Silvio |
| contents | 3D Gaussian Splatting (3D-GS) has recently emerged as a powerful technique for real-time, photorealistic rendering by optimizing anisotropic Gaussian primitives from view-dependent images. While 3D-GS has been extended to scientific visualization, prior work remains limited to single-GPU settings, restricting scalability for large datasets on high-performance computing (HPC) systems. We present a distributed 3D-GS pipeline tailored for HPC. Our approach partitions data across nodes, trains Gaussian splats in parallel using multi-nodes and multi-GPUs, and merges splats for global rendering. To eliminate artifacts, we add ghost cells at partition boundaries and apply background masks to remove irrelevant pixels. Benchmarks on the Richtmyer-Meshkov datasets (about 106.7M Gaussians) show up to 3X speedup across 8 nodes on Polaris while preserving image quality. These results demonstrate that distributed 3D-GS enables scalable visualization of large-scale scientific data and provide a foundation for future in situ applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_12138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization Han, Mengjiao Sewell, Andres Insley, Joseph Knowles, Janet Mateevitsi, Victor A. Papka, Michael E. Petruzza, Steve Rizzi, Silvio Distributed, Parallel, and Cluster Computing 3D Gaussian Splatting (3D-GS) has recently emerged as a powerful technique for real-time, photorealistic rendering by optimizing anisotropic Gaussian primitives from view-dependent images. While 3D-GS has been extended to scientific visualization, prior work remains limited to single-GPU settings, restricting scalability for large datasets on high-performance computing (HPC) systems. We present a distributed 3D-GS pipeline tailored for HPC. Our approach partitions data across nodes, trains Gaussian splats in parallel using multi-nodes and multi-GPUs, and merges splats for global rendering. To eliminate artifacts, we add ghost cells at partition boundaries and apply background masks to remove irrelevant pixels. Benchmarks on the Richtmyer-Meshkov datasets (about 106.7M Gaussians) show up to 3X speedup across 8 nodes on Polaris while preserving image quality. These results demonstrate that distributed 3D-GS enables scalable visualization of large-scale scientific data and provide a foundation for future in situ applications. |
| title | Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.12138 |