# Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering

arXiv: https://arxiv.org/abs/2504.13339 · HTML: https://mlsanity.com/paper/2504.13339/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2025.

## Built on by (most cited first, 6 of 6)

- [ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization](https://mlsanity.com/paper/2607.18466/) (2026)
- [Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation](https://mlsanity.com/paper/2607.01164/) (2026)
- [Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation](https://mlsanity.com/paper/2608.14112/) (2026)
- [A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes](https://mlsanity.com/paper/2607.28047/) (2026)
- [3D Field Data Reduction with Adaptive Sample-Based Gaussian-Encoded Reconstruction](https://mlsanity.com/paper/2609.16024/) (2026)
- [FactorSplat: Appearance-Controllable Gaussian Proxies for Medical Volume Rendering](https://mlsanity.com/paper/2610.02382/) (2026)

## Abstract (arXiv)

Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user systems. Novel view synthesis techniques can address this by producing a small, interactive model of the data, requiring only a set of training images to learn from. While these models allow accessible visualization of large data and complex scenes, they do not provide the interactions needed for scientific volumes, as they do not support interactive selection of transfer functions and lighting parameters. To address this, we introduce Volume Encoding Gaussians (VEG), a 3D Gaussian-based representation for volume visualization that supports arbitrary color and opacity mappings. Unlike prior 3D Gaussian Splatting (3DGS) methods that store color and opacity for each Gaussian, VEG decouple the visual appearance from the data representation by encoding only scalar values, enabling transfer function-agnostic rendering of 3DGS models. To ensure complete scalar field coverage, we introduce an opacity-guided training strategy, using differentiable rendering with multiple transfer functions to optimize our data representation. This allows VEG to preserve fine features across the full scalar range of a dataset while remaining independent of any specific transfer function. Across a diverse set of volume datasets, we demonstrate that our method outperforms the state-of-the-art on transfer functions unseen during training, while requiring a fraction of the memory and training time.