EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
2026-07-20 • Graphics
GraphicsDatabasesMachine Learning
AI summaryⓘ
The authors developed EVOLVE, a new method to compress large 3D scientific data efficiently. Unlike older methods that struggle to keep details or require separate models for different compression levels, EVOLVE uses an autoencoder trained on many types of scientific data to compress volumes better and faster. It also lets users adjust the compression level on the fly with one model. Tests show EVOLVE outperforms traditional compressors in both speed and quality on new datasets.
Lossy compressionVolumetric dataScientific simulationsAutoencoderImplicit neural representationsCompression ratioPerceptual hashingVariable-rate encodingReconstruction quality
Authors
Kaiyuan Tang, Maizhe Yang, Chaoli Wang
Abstract
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.