Super-Gaussian: Interactive Scene Editing for 3D Gaussian Splatting and NLI-Based Volume Visualization in Virtual Reality
Authors
Suemin Jeon (Korea University), Kaiyuan Tang (University of Notre Dame), Chaoli Wang (University of Notre Dame), Won-Ki Jeong (Korea University)
Presentation
- Session
- Turn up the volume(s)!
- Time
- Thursday, Nov 12, 13:00 – 13:12 (US/Eastern) · session 13:00 – 14:30
- Location
- Hall America north
Links
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Keywords
Volume visualization, virtual reality, Gaussian splatting, Gaussian selection, scene editing
Abstract
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, mitigating the computational latency typically associated with immersive 3D data exploration, thus 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 tedious point-by-point interaction. Building on this representation, 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 user-selected regions using NLI, allowing users to semantically query, interpret, and manipulate selected content within a visualization–perception–action loop. By integrating multimodal interaction, including speech, visual feedback, and spatial manipulation in VR, our framework supports intuitive and flexible exploration, editing, and scientific analysis of volumetric data. We demonstrate the effectiveness of Super-Gaussian through four representative case studies, quantitative selection benchmarks against existing Gaussian-based selection techniques, and system-level evaluations. Implementation details, experiments, and source code can be found on the project page: (https://smin0136.github.io/super-gaussian-project/).
For Practitioners
This paper may interest practitioners in scientific visualization, simulation science, medical imaging, and immersive analytics. It addresses the broader challenge of making complex volumetric data easier to explore, interpret, and communicate in interactive VR environments. Practitioners can learn how efficient Gaussian-based rendering, direct spatial interaction, and natural-language interfaces can be integrated to support flexible exploration and editing of large volumetric datasets.