Gaussian Volumetric Representation for Efficient Shear–Warp Visualization

Medical image visualization requires volumetric rendering al-gorithms that preserve anatomical fidelity while maintaining high ren-dering speeds. To address the high computational cost of large volumet-ric datasets, we propose a Gaussian-based volumetric representation forefficient visualization of dense medical volumes without compromisingstructural and radiometric details. We optimize the proposed represen-tation using Monte Carlo volumetric estimation, which enables trainingon a highly sparse subset of voxels while maintaining consistency withthe dense volumetric objective. In addition, we introduce a curriculumlearning strategy that progressively incorporates structured slice-basedsampling during training. Sparse voxel samples provide an early globalcoverage of the volume, while slice samples capture spatially correlatedregions that aid geometric structure and texture continuity. This combi-nation enables the Gaussian representation to learn anatomical detailsof various structures and corresponding textures from sparse supervisionwhile significantly reducing the computational cost associated with densevoxel processing. The learned representation supports slice-based ren-dering methods such as shear–warp volume rendering, enabling efficientvisualization of multimodal medical datasets including MRI and Cryosec-tion volumes while preserving anatomical structures. Using sparse super-vision, our method achieves up to 43.86 FPS rendering with a compres-sion ratio of 11.31:1.