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 cons
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- PossiblePossibly related (embedding) · 48%Autoencoders: Learning Through Reconstruction - Snowflake →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Kaiyuan Tang →
“EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database”
- LinkedLinked via arxiv author · 85%Maizhe Yang →
“EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database”
- LinkedLinked via arxiv author · 85%Chaoli Wang →
“EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database”