ClipGS：面向体积医学数据交互式电影化可视化的可裁剪高斯泼溅
ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data
AI总结：
本文提出ClipGS，一种支持裁剪平面的高斯泼溅框架，通过可学习截断和自适应调整模型，实现体积医学数据的交互式电影化可视化，在五个数据集上以156 FPS和16.1 MB模型达到36.635 PSNR，优于现有方法。
摘要：
体积医学数据的可视化对于提高诊断准确性、改善手术规划和教育至关重要。电影化渲染技术通过提供传达复杂解剖细节的高质量可视化，显著丰富了这一过程，从而促进医学背景下更好的理解和决策。然而，高计算成本和低渲染速度限制了交互式可视化在实际应用中的需求。在本文中，我们介绍了ClipGS，一种支持裁剪平面的创新高斯泼溅框架，用于体积医学数据的交互式电影化可视化。为了应对动态交互带来的挑战，我们提出了一种可学习的截断方案，该方案根据裁剪平面自动调整高斯原语的可见性。此外，我们还设计了一个自适应调整模型，以动态调整高斯的变形并优化渲染性能。我们在五个体积医学数据（包括CT和解剖切片数据）上验证了我们的方法，达到了平均36.635 PSNR的渲染质量，156 FPS的帧率和16.1 MB的模型大小，在渲染质量和效率上优于最先进的方法。
英文摘要：
The visualization of volumetric medical data is crucial for enhancing diagnostic accuracy and improving surgical planning and education. Cinematic rendering techniques significantly enrich this process by providing high-quality visualizations that convey intricate anatomical details, thereby facilitating better understanding and decision-making in medical contexts. However, the high computing cost and low rendering speed limit the requirement of interactive visualization in practical applications. In this paper, we introduce ClipGS, an innovative Gaussian splatting framework with the clipping plane supported, for interactive cinematic visualization of volumetric medical data. To address the challenges posed by dynamic interactions, we propose a learnable truncation scheme that automatically adjusts the visibility of Gaussian primitives in response to the clipping plane. Besides, we also design an adaptive adjustment model to dynamically adjust the deformation of Gaussians and refine the rendering performance. We validate our method on five volumetric medical data (including CT and anatomical slice data), and reach an average 36.635 PSNR rendering quality with 156 FPS and 16.1 MB model size, outperforming state-of-the-art methods in rendering quality and efficiency.
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