TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting
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Abstract (arXiv)
Advancements in volume visualization (VolVis) focus on extracting insights from 3D volumetric data by generating visually compelling renderings that reveal complex internal structures. Existing VolVis approaches have explored non-photorealistic rendering techniques to enhance the clarity, expressiveness, and informativeness of visual communication. While effective, these methods often rely on complex predefined rules and are limited to transferring a single style, restricting their flexibility. To overcome these limitations, we advocate the representation of VolVis scenes using differentiable Gaussian primitives combined with pretrained large models to enable arbitrary style transfer and real-time rendering. However, conventional 3D Gaussian primitives tightly couple geometry and appearance, leading to suboptimal stylization results. To address this, we introduce TexGS-VolVis, a textured Gaussian splatting framework for VolVis. TexGS-VolVis employs 2D Gaussian primitives, extending each Gaussian with additional texture and shading attributes, resulting in higher-quality, geometry-consistent stylization and enhanced lighting control during inference. Despite these improvements, achieving flexible and controllable scene editing remains challenging. To further enhance stylization, we develop image- and text-driven non-photorealistic scene editing tailored for TexGS-VolVis and 2D-lift-3D segmentation to enable partial editing with fine-grained control. We evaluate TexGS-VolVis both qualitatively and quantitatively across various volume rendering scenes, demonstrating its superiority over existing methods in terms of efficiency, visual quality, and editing flexibility.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 45 of its 72 references that are in the map. First posted on arXiv 18 Jul 2025.
Builds on
45 of 72 references.
References · 45
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33 more
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Built on by
Citing papers per year: 2025: 1; 2026: 8.
9 papers.
- 2026SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agentscited by 10
- 2026ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualizationcited by 3
- 2026Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualizationcited by 2
- 2026HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualizationcited by 2
- 2025KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenancecited by 1
- 2026SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualizationcited by 1
- 2026Lossless-INR: Lossless Volumetric Implicit Neural Representationscited by 0
- 2026EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Databasecited by 0
- 2026PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representationscited by 0
- 2026PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations3 Sep 2026
- 2026ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization20 Jul 2026
- 2026EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database20 Jul 2026
- 2026Lossless-INR: Lossless Volumetric Implicit Neural Representations20 Jul 2026
- 2026HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization25 Jun 2026
- 2026SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization4 Jun 2026
- 2026Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualization30 Apr 2026
- 2026SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents31 Mar 2026
- 2025KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance7 Oct 2025