# Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media

arXiv: https://arxiv.org/abs/2405.15425 · HTML: https://mlsanity.com/paper/2405.15425/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 94 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Mixture of Volumetric Primitives for Efficient Neural Rendering](https://mlsanity.com/paper/2103.01954/) (2021)
- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks](https://mlsanity.com/paper/2208.04448/) (2022)
- [A Radiative Transfer Framework for Spatially-Correlated Materials](https://mlsanity.com/paper/1805.02651/) (2018)
- [Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments](https://mlsanity.com/paper/2212.06125/) (2022)
- [An unbiased ray-marching transmittance estimator](https://mlsanity.com/paper/2102.10294/) (2021)
- [Primary-Space Adaptive Control Variates using Piecewise-Polynomial Approximations](https://mlsanity.com/paper/2008.06722/) (2020)
- [A Learned Radiance-Field Representation for Complex Luminaires](https://mlsanity.com/paper/2207.05009/) (2022)
- [Three-dimensional Epanechnikov mixture regression in image coding](https://mlsanity.com/paper/2106.01643/) (2021)

## Built on by (most cited first, 30 of 37)

- [3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting](https://mlsanity.com/paper/2412.12507/) (2024)
- [Radiant Foam: Real-Time Differentiable Ray Tracing](https://mlsanity.com/paper/2502.01157/) (2025)
- [Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?](https://mlsanity.com/paper/2502.19318/) (2025)
- [StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2503.24366/) (2025)
- [7DGS: Unified Spatial-Temporal-Angular Gaussian Splatting](https://mlsanity.com/paper/2503.07946/) (2025)
- [Faster-GS: Analyzing and Improving Gaussian Splatting Optimization](https://mlsanity.com/paper/2602.09999/) (2026)
- [6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric Rendering](https://mlsanity.com/paper/2410.04974/) (2024)
- [Volumetrically Consistent 3D Gaussian Rasterization](https://mlsanity.com/paper/2412.03378/) (2024)
- [AAA-Gaussians: Anti-Aliased and Artifact-Free 3D Gaussian Rendering](https://mlsanity.com/paper/2504.12811/) (2025)
- [3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras](https://mlsanity.com/paper/2505.24053/) (2025)
- [SOF: Sorted Opacity Fields for Fast Unbounded Surface Reconstruction](https://mlsanity.com/paper/2506.19139/) (2025)
- [GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting](https://mlsanity.com/paper/2507.19718/) (2025)
- [Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction](https://mlsanity.com/paper/2604.01204/) (2026)
- [SAGOnline: Segment Any Gaussians Online](https://mlsanity.com/paper/2508.08219/) (2025)
- [Power Foam: Unifying Real-Time Differentiable Ray Tracing and Rasterization](https://mlsanity.com/paper/2604.24994/) (2026)
- [Real-time Global Illumination for Dynamic 3D Gaussian Scenes](https://mlsanity.com/paper/2503.17897/) (2025)
- [Photoreal Scene Reconstruction from an Egocentric Device](https://mlsanity.com/paper/2506.04444/) (2025)
- [PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting](https://mlsanity.com/paper/2508.00259/) (2025)
- [Moment-Based 3D Gaussian Splatting: Resolving Volumetric Occlusion with Order-Independent Transmittance](https://mlsanity.com/paper/2512.11800/) (2025)
- [Radioactive 3D Gaussian Ray Tracing for Tomographic Reconstruction](https://mlsanity.com/paper/2602.01057/) (2026)
- [Gabor Fields: Orientation-Selective Level-of-Detail for Volume Rendering](https://mlsanity.com/paper/2602.05081/) (2026)
- [REdiSplats: Ray Tracing for Editable Gaussian Splatting](https://mlsanity.com/paper/2503.12284/) (2025)
- [The Impact and Outlook of 3D Gaussian Splatting](https://mlsanity.com/paper/2510.26694/) (2025)
- [GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering](https://mlsanity.com/paper/2601.20429/) (2026)
- [EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing](https://mlsanity.com/paper/2601.23065/) (2026)
- [Stochastic Ray Tracing for the Reconstruction of 3D Gaussian Splatting](https://mlsanity.com/paper/2603.23637/) (2026)
- [Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction](https://mlsanity.com/paper/2606.09794/) (2026)
- [Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat](https://mlsanity.com/paper/2609.07557/) (2026)
- [Projecting Gaussian Ellipsoids While Avoiding Affine Projection Approximation](https://mlsanity.com/paper/2411.07579/) (2024)
- [From Particles to Fields: Reframing Photon Mapping with Continuous Gaussian Photon Fields](https://mlsanity.com/paper/2512.12459/) (2025)

## Abstract (arXiv)

Efficient scene representations are essential for many computer graphics applications. A general unified representation that can handle both surfaces and volumes simultaneously, remains a research challenge. Inspired by recent methods for scene reconstruction that leverage mixtures of 3D Gaussians to model radiance fields, we formalize and generalize the modeling of scattering and emissive media using mixtures of simple kernel-based volumetric primitives. We introduce closed-form solutions for transmittance and free-flight distance sampling for different kernels, and propose several optimizations to use our method efficiently within any off-the-shelf volumetric path tracer. We demonstrate our method as a compact and efficient alternative to other forms of volume modeling for forward and inverse rendering of scattering media. Furthermore, we adapt and showcase our method in radiance field optimization and rendering, providing additional flexibility compared to current state of the art given its ray-tracing formulation. We also introduce the Epanechnikov kernel and demonstrate its potential as an efficient alternative to the traditionally-used Gaussian kernel in scene reconstruction tasks. The versatility and physically-based nature of our approach allows us to go beyond radiance fields and bring to kernel-based modeling and rendering any path-tracing enabled functionality such as scattering, relighting and complex camera models.