发表机构
Shenzhen University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（深圳大学; 广东省人工智能与数字经济实验室（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TR-GS用学生t分布基元与射线置信度模型优化3D高斯溅射，实现高保真稀疏视图CT体积渲染，在多数场景优于基线，可支撑下游医学多媒体应用
AI 中文摘要
高保真三维医学可视化支持临床评估、手术规划等应用。稀疏视图计算机断层扫描（CT）可减少投影需求及相关辐射暴露，但有限观测可能引入结构伪影与重建不确定性。尽管三维高斯溅射（3DGS）为体积渲染提供了高效显式表示，现有基于标准高斯基元的CT方法对稀疏视图采集下的不可靠观测较为敏感。本文提出TR-GS，一种用于稀疏视图CT体积渲染的高斯溅射框架：TR-GS用可投影的学生t分布基元替代标准高斯基元，引入射线置信度模型，依据局部射线可观测性调控其自由度；还采用置信度引导的三维小波正则化，平衡高频细节保留与噪声抑制。在合成及真实数据集上的实验表明，TR-GS在多数评估设置下优于代表性基线，其余场景仍具竞争力。所得体积表示可支持扩展现实（XR）可视化、交互式临床渲染等下游医学多媒体应用。
英文摘要
High-fidelity 3D medical visualization supports applications such as clinical assessment and surgical planning. Sparse-view computed tomography (CT) can reduce projection requirements and associated radiation exposure, but limited observations may introduce structural artifacts and reconstruction uncertainty. Although 3D Gaussian Splatting (3DGS) provides an efficient explicit representation for volumetric rendering, existing CT methods based on standard Gaussian primitives may be sensitive to unreliable observations under sparse-view acquisition. We present TR-GS, a Gaussian-splatting framework for sparse view CT volumetric rendering. TR-GS replaces standard Gaussian primitives with projectable Student's t-distribution primitives and introduces a ray-confidence model that regulates their degrees of freedom according to local ray observability. Confidence-guided 3D wavelet regularization is further used to balance high-frequency detail preservation and noise suppression. This work is licensed under a Creative Commons Attribution 4.0 International License. Experiments on synthetic and real-world datasets show that TR-GS improves over representative baselines in most evaluated settings and remains competitive in the remaining cases. The resulting volumetric representations may support downstream medical multimedia applications, including XR-based visualization and interactive clinical rendering.
Journal refACM Multimedia 2026
