# Gaussian Volumetric Representation for Efficient Shear-Warp Visualization

arXiv: https://arxiv.org/abs/2607.25377 · HTML: https://mlsanity.com/paper/2607.25377/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Neural Importance Sampling](https://mlsanity.com/paper/1808.03856/) (2018)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [Vision Transformer for NeRF-Based View Synthesis from a Single Input Image](https://mlsanity.com/paper/2207.05736/) (2022)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration](https://mlsanity.com/paper/2403.11056/) (2024)
- [MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray](https://mlsanity.com/paper/2202.01020/) (2022)
- [Curriculum DeepSDF](https://mlsanity.com/paper/2003.08593/) (2020)
- [Multi-view 3D Reconstruction with Transformer](https://mlsanity.com/paper/2103.12957/) (2021)
- [FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction](https://mlsanity.com/paper/2412.09573/) (2024)
- [Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media](https://mlsanity.com/paper/2405.15425/) (2024)
- [ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction](https://mlsanity.com/paper/2305.18832/) (2023)
- [Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts](https://mlsanity.com/paper/2308.11793/) (2023)
- [A Survey of Monte Carlo Methods for Parameter Estimation](https://mlsanity.com/paper/2107.11820/) (2021)
- [iVR-GS: Inverse Volume Rendering for Explorable Visualization via Editable 3D Gaussian Splatting](https://mlsanity.com/paper/2504.17954/) (2025)
- [CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs](https://mlsanity.com/paper/2403.16885/) (2024)
- [Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization](https://mlsanity.com/paper/2410.16978/) (2024)
- [ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field](https://mlsanity.com/paper/2303.13817/) (2023)
- [DT-NeRF: Decomposed Triplane-Hash Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis](https://mlsanity.com/paper/2309.07752/) (2023)
- [Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering](https://mlsanity.com/paper/2210.08080/) (2022)
- [ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data](https://mlsanity.com/paper/2507.06647/) (2025)
- [GAT-NeRF: Geometry-Aware-Transformer Enhanced Neural Radiance Fields for High-Fidelity 4D Facial Avatars](https://mlsanity.com/paper/2601.14875/) (2026)

## Abstract (arXiv)

Medical image visualization requires volumetric rendering algorithms that preserve anatomical fidelity while maintaining high rendering speeds. To address the high computational cost of large volumetric datasets, we propose a Gaussian-based volumetric representation for efficient visualization of dense medical volumes without compromising structural and radiometric details. We optimize the proposed representation using Monte Carlo volumetric estimation, which enables training on a highly sparse subset of voxels while maintaining consistency with the dense volumetric objective. In addition, we introduce a curriculum learning strategy that progressively incorporates structured slice-based sampling during training. Sparse voxel samples provide an early global coverage of the volume, while slice samples capture spatially correlated regions that aid geometric structure and texture continuity. This combination enables the Gaussian representation to learn anatomical details of various structures and corresponding textures from sparse supervision while significantly reducing the computational cost associated with dense voxel processing. The learned representation supports slice-based rendering methods such as shear-warp volume rendering, enabling efficient visualization of multimodal medical datasets including MRI and Cryosection volumes while preserving anatomical structures. Using sparse supervision, our method achieves up to 43.86 FPS rendering with a compression ratio of 11.31:1.