GS-Verse：基于网格的Gaussian Splatting用于虚拟现实中的物理感知交互
GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Reality
AI总结：
本文提出GS-Verse方法，通过将物体网格直接与Gaussian Splatting表示集成，实现虚拟现实中精确的表面近似与高度逼真的物理感知交互，并在18人用户研究中显著优于现有技术。
摘要：
随着对沉浸式3D内容需求的增长，对直观高效交互方法的需求变得至关重要。当前在虚拟现实（VR）中物理操纵3D内容的技术通常面临重大局限，包括依赖工程密集型流程以及简化的几何表示（如四面体笼），这可能会损害视觉保真度和物理准确性。在本文中，我们介绍了GS-Verse（用于虚拟环境渲染和场景编辑的Gaussian Splatting），这是一种新颖的方法，旨在通过将物体的网格直接与Gaussian Splatting（GS）表示集成来克服这些挑战。我们的方法实现了更精确的表面近似，从而产生高度逼真的变形和交互。通过利用现有的3D网格资产，GS-Verse促进了无缝的内容重用并简化了开发工作流程。此外，我们的系统被设计为与物理引擎无关，赋予开发者强大的部署灵活性。这种多功能架构提供了一种高度逼真、适应性强且直观的交互式3D操纵方法。我们在一项涉及18名参与者的比较用户研究中，将我们的方法与当前将VR与GS结合的最先进技术进行了严格验证。具体而言，我们证明我们的方法在物理感知的拉伸操纵方面具有统计学上的显著优势，并且在其他基于物理的操纵（如扭曲和摇动）中也表现出更高的一致性。跨各种交互和场景的进一步评估证实，我们的方法始终提供高且可靠的性能，展示了其作为现有方法可行替代方案的潜力。
英文摘要：
As the demand for immersive 3D content grows, the need for intuitive and efficient interaction methods becomes paramount. Current techniques for physically manipulating 3D content within Virtual Reality (VR) often face significant limitations, including reliance on engineering-intensive processes and simplified geometric representations, such as tetrahedral cages, which can compromise visual fidelity and physical accuracy. In this paper, we introduce GS-Verse (Gaussian Splatting for Virtual Environment Rendering and Scene Editing), a novel method designed to overcome these challenges by directly integrating an object's mesh with a Gaussian Splatting (GS) representation. Our approach enables more precise surface approximation, leading to highly realistic deformations and interactions. By leveraging existing 3D mesh assets, GS-Verse facilitates seamless content reuse and simplifies the development workflow. Moreover, our system is designed to be physics-engine-agnostic, granting developers robust deployment flexibility. This versatile architecture delivers a highly realistic, adaptable, and intuitive approach to interactive 3D manipulation. We rigorously validate our method against the current state-of-the-art technique that couples VR with GS in a comparative user study involving 18 participants. Specifically, we demonstrate that our approach is statistically significantly better for physics-aware stretching manipulation and is also more consistent in other physics-based manipulations like twisting and shaking. Further evaluation across various interactions and scenes confirms that our method consistently delivers high and reliable performance, showing its potential as a plausible alternative to existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
