这篇论文提出了EVOLVE，用自编码器压缩科学体数据，压缩比高还支持变码率，比传统方法强很多。
EVOLVE是一个基于自编码器的体数据压缩框架，针对高压缩比下的离线压缩优化。该框架构建了包含6,376个体数据的跨领域数据库，覆盖21项科学模拟，并通过感知哈希确保多样性。其可学习增益机制和三阶段训练策略使单一模型能在推理时连续调整压缩比。在多个未见过的科学模拟数据集上，EVOLVE在同等重建质量下实现比传统压缩器更高的压缩比，压缩速度比基于隐式神经表示的方法快数个数量级。
EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
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.