Gaussian Volumetric Representation for Efficient Shear-Warp Visualization
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 s...
| Authors | Mayuri Mathur, Ojaswa Sharma |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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