用3D高斯模拟相机，优化视角以更真实合成新视图。
Camera Splatting for Continuous View Optimization
- 将相机建模为3D高斯点（相机点），通过可微方式持续优化
- 相比最远视角采样，在金属反光和文字纹理等细节上表现更好
- 适合需要高质量视图合成的场景，如工业检测或虚拟拍摄
我们提出一种新型视图优化框架——相机点化（Camera Splatting），用于新视图合成。每个相机被建模为一个3D高斯，称为相机点；在表面附近采样的3D点上放置虚拟相机（点相机），用于观察相机点的分布。通过连续且可微地优化相机点，使点相机观测到期望的目标分布，方法类似原始3D高斯点化。与最远视角采样（FVS）相比，该优化视图在捕捉复杂视依赖现象（如强烈金属反光和精细纹理如文字）方面表现更优。
原文摘要 · Abstract (English)
We propose Camera Splatting, a novel view optimization framework for novel view synthesis. Each camera is modeled as a 3D Gaussian, referred to as a camera splat, and virtual cameras, termed point cameras, are placed at 3D points sampled near the surface to observe the distribution of camera splats. View optimization is achieved by continuously and differentiably refining the camera splats so that desirable target distributions are observed from the point cameras, in a manner similar to the original 3D Gaussian splatting. Compared to the Farthest View Sampling (FVS) approach, our optimized views demonstrate superior performance in capturing complex view-dependent phenomena, including intense metallic reflections and intricate textures such as text.
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