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

Landon Dyken, Andres Sewell, Will Usher, Nathan Debardeleben, Steve Petruzza, Sidharth Kumar

In IEEE Pacific Visualization Conference (To Appear), 2026.

**Fig. 1:** Qualitative showcase of novel view synthesis for a VEG model on complex unseen transfer functions. We train a model for the Paraview head dataset using the five transfer functions on the left, and test with those and the four functions on the right (A-D), where the opacity and color maps were unseen during training. The top, middle, and bottom rows of images show ground truth, inference, and difference images.

Abstract

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 a dataset’s full scalar range 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.

Content

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arXiv

BibTeX

      @inproceedings{dyken_veg26,
  author={Dyken, Landon and Sewell, Andres and Usher, Will and Debardeleben, Nathan and Petruzza, Steve and Kumar, Sidharth},
  booktitle={IEEE Pacific Visualization Conference (To Appear)},
  title={Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering},
  year={2026},
  }