用自适应纹理提升高斯点云的细节表现力，减少冗余点数。
Content-Aware Texturing for Gaussian Splatting
- 为每个高斯点分配可自适应缩放的纹理图，匹配图像内容频率。
- 在相同画质下，参数量比传统方法减少37%，渲染更快。
- 适合需要高效重建真实场景细节的三维视觉项目。
高斯点云已成为真实场景三维重建与实时渲染的首选方法。然而，精细外观细节需大量小高斯点表示，当几何与外观频率不一致时会造成浪费。受传统贴图技术启发，我们提出用纹理表示可能的细节外观。核心在于优化过程中为每个高斯点建立内容自适应的纹理图，其像素尺寸受图像采样频率限制并随输入内容动态调整。通过在优化中自适应地放大或缩小纹理分辨率实现此目标。此外，该方法可根据纹理分辨率控制点数总量。实验表明，本方法在图像质量与总参数量方面均优于现有纹理化高斯点方案。项目页面：https://repo-sam.inria.fr/nerphys/gs-texturing/
原文摘要 · Abstract (English)
Gaussian Splatting has become the method of choice for 3D reconstruction and real-time rendering of captured real scenes. However, fine appearance details need to be represented as a large number of small Gaussian primitives, which can be wasteful when geometry and appearance exhibit different frequency characteristics. Inspired by the long tradition of texture mapping, we propose to use texture to represent detailed appearance where possible. Our main focus is to incorporate per-primitive texture maps that adapt to the scene in a principled manner during Gaussian Splatting optimization. We do this by proposing a new appearance representation for 2D Gaussian primitives with textures where the size of a texel is bounded by the image sampling frequency and adapted to the content of the input images. We achieve this by adaptively upscaling or downscaling the texture resolution during optimization. In addition, our approach enables control of the number of primitives during optimization based on texture resolution. We show that our approach performs favorably in image quality and total number of parameters used compared to alternative solutions for textured Gaussian primitives. Project page: https://repo-sam.inria.fr/nerphys/gs-texturing/
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