Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
arXiv:2607.01164v1 Announce Type: new Abstract: 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 a
This work is emerging now as implicit neural representations and 3D Gaussian splatting have matured, providing the foundational techniques for more efficient data compression.
Efficient compression of 3D data is crucial for scaling AI applications, especially in areas like robotics, metaverse development, and scientific simulations where large volumetric datasets are common.
This research introduces a novel method that could significantly reduce memory footprint for structured and unstructured 3D volume data, improving efficiency and accessibility of complex volumetric models.
- · AI developers
- · Robotics companies
- · Gaming and metaverse platforms
- · Scientific simulation platforms
- · Providers of less efficient 3D data compression solutions
- · Systems with limited memory for 3D data
Reduced computational overhead and storage requirements for complex 3D datasets.
Faster development and deployment of real-time 3D AI applications and simulations.
Accelerated progress in autonomous systems and virtual world development due to more manageable 3D data handling.
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Read at arXiv cs.LG