发表机构
Department of Computer Science and Engineering, University of Notre Dame（计算机科学与工程系，诺特丹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对科学模拟体数据压缩难题，EVOLVE构建跨域数据库，改进自动编码器设计，采用可学习增益机制实现可变速率编码，实验证明其在压缩率和速度上优于传统方法与隐式神经表示方法。
AI 中文摘要
大规模科学模拟生成体数据的速度远超存储和网络带宽的发展，有效有损压缩愈发关键。传统压缩器在高压缩率下难以保留精细结构细节，隐式神经表示需昂贵的逐体优化且压缩率固定。为此提出EVOLVE，一个基于自动编码器的体压缩框架，有三个关键贡献：构建大规模跨域数据库，重新审视基于自动编码器的压缩器设计空间并改进，开发可学习增益机制实现可变速率编码。实验表明，EVOLVE在可比重建质量下压缩率远超传统压缩器，速度比基于隐式神经表示的方法快几个数量级。
英文摘要
Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical. However, conventional compressors often struggle to preserve fine structural details at high compression ratios (CRs), and implicit neural representations (INRs) require costly per-volume optimization and produce models with fixed CRs. To respond, we present EVOLVE, an autoencoder (AE)-based volume-compression framework that targets high CRs for offline compression, with three key contributions. First, we construct a large-scale cross-domain database of 6,376 volumes from 21 scientific simulations, curated via perceptual hashing to ensure diversity, enabling the optimized model to extract features that generalize across volumes within the covered scientific simulation domains. Second, we reexamine the design space of AE-based compressors and incorporate several macro- and micro-designs into a vanilla AE to develop EVOLVE, which substantially improves the expressive power and compression capability. Third, we develop a learnable gain mechanism with a three-stage training strategy to enable variable-rate encoding, allowing a single model to support continuous CR adjustment at inference time. Experiments on multiple unseen scientific simulation datasets demonstrate that EVOLVE achieves substantially higher CRs than conventional compressors at comparable reconstruction quality, while delivering compression speeds that are orders of magnitude faster than INR-based methods, highlighting its promise as a strong alternative for compressing scientific data. The code, model weights, and results are available on our project page at https://evolve-vis.github.io.
CommentsTo be published in Proceedings of IEEE VIS 2026, IEEE Transactions on Visualization and Computer Graphics
