Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization

2025-09-15

AI Summary

Existing 3D Gaussian Splatting (3D-GS) methods are constrained by single-GPU architectures, hindering scalable high-resolution isosurface visualization of large-scale scientific datasets in HPC environments. Method: We propose the first HPC-oriented distributed 3D Gaussian lattice rendering framework, featuring data-parallel domain decomposition, multi-node joint training, and global Gaussian primitive fusion to enable a scalable end-to-end rendering pipeline. We further introduce ghost-cell padding at partition boundaries and background-mask-guided artifact suppression—novel mechanisms ensuring stable distributed training and consistent global rendering across multiple GPUs and nodes. Results: Evaluated on the Richtmyer–Meshkov dataset (106.7 million Gaussians), our framework achieves up to 3× speedup on an 8-node Polaris cluster while preserving image fidelity. This work significantly advances the practical deployment of 3D-GS for in-situ scientific visualization.

Technology Category

• Computer Vision: 3D Computer Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Enabling scalable 3D Gaussian Splatting for large scientific datasets
• Distributing computation across multi-node HPC systems efficiently
• Eliminating rendering artifacts in parallel Gaussian splat training

Innovation

• Distributed multi-node multi-GPU parallel training
• Ghost cells added at partition boundaries
• Background masks remove irrelevant pixels