AI 中文总结
针对大规模粒子模拟带来的存储等压力及现有压缩器问题，提出基于3D高斯点渲染的ParticleGS框架，结合多种技术，在HACC模拟中表现出色，压缩率高且渲染速度快，并能推广到其他模拟。
AI 中文摘要
大规模粒子模拟产生数亿粒子，给存储、传输和交互式可视化带来压力。现有有损压缩器如SZ3在数据空间运行，无法保证下游可视化保真度。我们提出ParticleGS，这是一个基于3D高斯点渲染的可视化感知框架，它学习直接针对渲染图像质量优化的紧凑表示，结合多阶段、多轨道训练管道、VizMapper轻量级网络和空间块训练。在一个2.81亿粒子的HACC宇宙学模拟中，我们的8块模型在65倍压缩率下达到30.03 dB PSNR，比SZ3在可比比率下高5 - 8 dB，且无需调整即可推广到其他HACC区域和仅暗物质的FIRE - 2模拟。它在单个GPU上以662 FPS渲染，比ParaView处理完整粒子数据快2300倍以上。
英文摘要
Large-scale particle simulations produce hundreds of millions of particles, straining storage, transfer, and interactive visualization. Existing lossy compressors such as SZ3 operate in data space and provide no guarantees on downstream visualization fidelity. We propose ParticleGS, a visualization-aware framework based on 3D Gaussian Splatting (3DGS) that learns a compact representation directly optimized for rendered image quality, combining (1) a multi-stage, multi-orbit training pipeline, (2) VizMapper, a lightweight network that adapts a single trained model to user-specified visualization parameters at inference time, and (3) spatial block training with KD-tree decomposition and global fine-tuning. On a 281-million-particle HACC cosmological simulation, our 8-block model reaches 30.03 dB PSNR at 65x compression, outperforming SZ3 by 5-8 dB at comparable ratios, and generalizes without tuning to additional HACC regions and a dark-matter-only FIRE-2 simulation. It renders at 662 FPS on a single GPU, over 2,300x faster than ParaView on the full particle data.
Comments13 pages, 13 figures. Accepted at SC26 (The International Conference for High Performance Computing, Networking, Storage and Analysis)
