REdiSplats：面向可编辑 Gaussian Splatting 的光线追踪
REdiSplats: Ray Tracing for Editable Gaussian Splatting
AI总结：
针对经典 Gaussian Splatting 难以处理光照、编辑和物理仿真的问题，本文提出 REdiSplats，用网格参数化平面高斯并结合光线追踪，实现可编辑、可物理仿真且兼容 Blender 与 Nvdiffrast 的渲染。
摘要：
Gaussian Splatting（GS）已成为最重要的神经渲染算法之一。GS 使用具有可训练颜色和不透明度的高斯分量来表示三维场景。这种表示能够以快速推理实现高质量渲染。遗憾的是，将这种解决方案与不断变化的光照条件（包括阴影和光反射）、手动调整以及物理引擎相结合颇具挑战。最近，已经出现了一些将光线追踪或网格基元纳入 GS 的方法，以解决其中部分缺陷。然而，目前还没有一种解决方案能够同时解决经典 GS 的所有现存局限。因此，我们提出 REdiSplats，它采用光线追踪以及平面三维高斯的基于网格的表示。在实践中，我们使用由网格参数化的平面高斯分布对场景进行建模。我们可以利用快速光线追踪，并通过调整网格顶点来控制高斯修改。此外，REdiSplats 支持对光照条件、手动调整和物理仿真进行建模。进一步地，我们可以使用 Blender 或 Nvdiffrast 等三维工具渲染模型，这为将其与所有面向网格表示的现有三维图形技术相集成提供了可能。
英文摘要：
Gaussian Splatting (GS) has become one of the most important neural rendering algorithms. GS represents 3D scenes using Gaussian components with trainable color and opacity. This representation achieves high-quality renderings with fast inference. Regrettably, it is challenging to integrate such a solution with varying light conditions, including shadows and light reflections, manual adjustments, and a physical engine. Recently, a few approaches have appeared that incorporate ray-tracing or mesh primitives into GS to address some of these caveats. However, no such solution can simultaneously solve all the existing limitations of the classical GS. Consequently, we introduce REdiSplats, which employs ray tracing and a mesh-based representation of flat 3D Gaussians. In practice, we model the scene using flat Gaussian distributions parameterized by the mesh. We can leverage fast ray tracing and control Gaussian modification by adjusting the mesh vertices. Moreover, REdiSplats allows modeling of light conditions, manual adjustments, and physical simulation. Furthermore, we can render our models using 3D tools such as Blender or Nvdiffrast, which opens the possibility of integrating them with all existing 3D graphics techniques dedicated to mesh representations.
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。
