# 3D Gaussian Modeling and Ray Marching of OpenVDB datasets for Scientific Visualization

arXiv: https://arxiv.org/abs/2509.11377 · HTML: https://mlsanity.com/paper/2509.11377/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [3D Field Data Reduction with Adaptive Sample-Based Gaussian-Encoded Reconstruction](https://mlsanity.com/paper/2609.16024/) (2026)

## Abstract (arXiv)

3D Gaussians are currently being heavily investigated for their scene modeling and compression abilities. In 3D volumes, their use is being explored for representing dense volumes as sparsely as possible. However, most of these methods begin with a memory inefficient data format. Specially in Scientific Visualization(SciVis), where most popular formats are dense-grid data structures that store every grid cell, irrespective of its contribution. OpenVDB library and data format were introduced for representing sparse volumetric data specifically for visual effects use cases such as clouds, fire, fluids etc. It avoids storing empty cells by masking them during storage. It presents an opportunity for use in SciVis, specifically as a modeling framework for conversion to 3D Gaussian particles for further compression and for a unified modeling approach for different scientific volume types. This compression head-start is non-trivial and this paper would like to present this with a rendering algorithm based on line integration implemented in OptiX8.1 for calculating 3D Gaussians contribution along a ray for optical-depth accumulation. For comparing the rendering results of our ray marching Gaussians renderer, we also implement a SciVis style primary-ray only NanoVDB HDDA based ray marcher for OpenVDB voxel grids. Finally, this paper also explores application of this Gaussian model to formats of volumes other than regular grids, such as AMR volumes and point clouds, using internal representation of OpenVDB grid class types for data hierarchy and subdivision structure.