AI 中文总结
本研究针对VR中体可视化的渲染成本与交互问题，提出Super-Gaussian框架，结合三维高斯溅射、特征感知聚类、分层选择-细化工作流与NLI，实现高效直观的体数据交互编辑与分析
AI 中文摘要
尽管虚拟现实（VR）有望实现直观的空间交互，但VR中的体可视化（VolVis）仍受限于高渲染成本和运动不适。近期进展表明，用三维高斯溅射（3D Gaussian splatting）表示体场景可实现高性能渲染，该表示非常适合VR。然而，现有基于高斯的场景编辑工作流仍受限于缓慢的离线分割和易引发疲劳的手动选择。为应对这些挑战，我们提出Super-Gaussian，一种新型VolVis框架，通过直观的三维高斯选择和自然语言交互（NLI）增强VR中的场景编辑与交互。我们的方法通过特征感知聚类将高斯基元分组为更高级单元，无需逐点交互即可高效选择复杂体区域，如医学图像中的肿瘤或宇宙学数据中的丝状物。在此基础上，我们引入分层选择-细化工作流，结合基于随机游走的区域传播、聚类选择和点细化，使用户能以更少精力逐步指定感兴趣区域。我们进一步支持使用NLI对选定区域进行即时文本标注，允许用户在可视化-感知-行动循环内对内容进行语义查询、解释和操作。通过整合VR中的多模态交互，包括语音、视觉反馈和空间操作，我们的框架支持对体数据进行直观探索、编辑和科学分析。我们通过四个案例研究、与现有基于高斯技术的定量选择基准测试以及系统级评估证明Super-Gaussian的有效性。实现细节和实验可在项目页面查看：this https URL
英文摘要
Despite the promise of virtual reality (VR) for intuitive spatial interaction, volume visualization (VolVis) in VR remains constrained by high rendering costs and motion discomfort. Recent advances have shown that representing volumetric scenes with 3D Gaussian splatting enables high-performance rendering, making this representation well-suited for VR. However, existing Gaussian-based scene editing workflows remain limited by slow offline segmentation and fatigue-inducing manual selection. To address these challenges, we present Super-Gaussian, a novel VolVis framework that enhances scene editing and interaction in VR through intuitive 3D Gaussian selection and natural language interaction (NLI). Our approach groups Gaussian primitives into higher-level units via feature-aware clustering, enabling efficient selection of complex volumetric regions, such as tumors in medical images or filaments in cosmological data, without point-by-point interaction. Building on this, we introduce a hierarchical select-and-refine workflow that combines random-walk-based region propagation, cluster selection, and point refinement, allowing users to progressively specify regions of interest with reduced effort. We further support on-the-fly text labeling of selected regions using NLI, allowing users to semantically query, interpret, and manipulate content within a visualization-perception-action loop. By integrating multimodal interaction, including speech, visual feedback, and spatial manipulation in VR, our framework supports intuitive exploration, editing, and scientific analysis of volumetric data. We demonstrate the effectiveness of Super-Gaussian through four case studies, quantitative selection benchmarks against existing Gaussian-based techniques, and system-level evaluations. Implementation details and experiments can be found on the project page: https://smin0136.github.io/super-gaussian-project/
CommentsIEEE VIS 2026 accepted
