Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint. However, as existing INRs for unstructured volumes do not encode geometry, they require partial mesh storage for later sampling, limiting achievable compression. At the same time, novel view synthesis methods have shown that explicit collections of 3D Gaussians can be used to accurately visualize volume data. In this work, we introduce an explicit model for volume data compression bas
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- Linked via arxiv authorLandon Dyken →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorSharmistha Chakrabarti →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorNathan Debardeleben →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorSteve Petruzza →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorQi Wu →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorWill Usher →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
- Linked via arxiv authorSidharth Kumar →
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation