Lossless-INR: Lossless Volumetric Implicit Neural Representations
Implicit neural representation (INR) methods provide continuous coordinate-to-value mappings and integrate naturally with direct volume rendering, making them attractive for representing volumetric data. However, existing INR-based approaches for volumetric data are inherently lossy, and even small reconstruction errors can propagate through rendering and downstream analysis. In this work, we explore Lossless-INR, a lossless INR framework for 3D scientific volumetric data based on bit-plane decomposition. By decomposing each voxel value into binary bit-planes, we reformulate reconstruction as
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- 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 →
“Lossless-INR: Lossless Volumetric Implicit Neural Representations”
- LinkedLinked via arxiv author · 85%Daniel Burke →
“Lossless-INR: Lossless Volumetric Implicit Neural Representations”
- LinkedLinked via arxiv author · 85%Chaoli Wang →
“Lossless-INR: Lossless Volumetric Implicit Neural Representations”