发表机构
KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对基于Splat的CT在实际稀疏视图下的伪影问题，分析其源于姿态不准确性，推导了联合优化几何参数的稳定梯度框架，提升了重建保真度且易于集成。
AI 中文摘要
X射线计算机断层扫描（CT）通过将X射线穿透目标获得的投影图像重建出物体的体积表示。近期的基于Splat的断层扫描技术将体积表示为三维高斯的连续分布，在锥束稀疏视图CT中展现出高重建质量与快速收敛的特性。然而，当部署到视图分布有限且不均匀的实际CT系统中时，我们观察到其产生的明显条纹和条状伪影远多于传统重建方法。通过详细分析，我们表明这些伪影主要源于采集几何结构中的姿态不准确性，而非视图稀疏性本身。我们重新探讨了Splat公式中的姿态敏感性，并推导了一种稳定的基于梯度的框架，该框架可在重建过程中联合优化几何参数。本研究不仅明确了姿态扰动如何通过可微投影算子传播，还揭示了基于Splat的CT为何对几何错位格外敏感。所得公式保持轻量特性，易于集成到现有流程中，同时在实际稀疏视图条件下大幅提升了重建保真度。
英文摘要
X-ray computed tomography (CT) reconstructs volumetric representations of objects from projection images obtained by transmitting X-rays through a target. Recent splat-based tomography, which represents a volume as a continuous distribution of 3D Gaussians, has demonstrated both high reconstruction quality and fast convergence in cone-beam sparse-view CT. However, when deployed in real CT systems with limited and non-uniform view distributions, we observe distinctive streak and strip artifacts that are far more pronounced than in conventional reconstruction methods. Through detailed analysis, we show that these artifacts primarily originate from pose inaccuracies in the acquisition geometry rather than from view sparsity itself. We revisit pose sensitivity in the splatting formulation and derive a stable gradient-based framework that jointly refines geometric parameters during reconstruction. Our study not only identifies how pose perturbations propagate through the differentiable projection operator but also reveals why splat-based CT is particularly vulnerable to geometric misalignment. The resulting formulation remains lightweight and easily integrable into existing pipelines while substantially improving reconstruction fidelity under real-world sparse-view conditions.
Journal refProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
