发表机构
Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TV-SGS通过张量投票实现高斯泼溅间的直接通信，提出不依赖渲染的三维损失，在稀疏视图下改善场景几何，同时保持或提升渲染质量。
AI 中文摘要
高斯泼溅在推断场景表示方面已展现出有效性，尤其擅长于新视角合成。多个泼溅点协同无缝地合成新视角的像素，并在联合优化过程中相互影响，尽管它们仅通过共同投影到的像素间接地彼此作用。我们提出了一种方法，使泼溅点之间能够直接通信，以增强它们在三维空间中形成的几何结构。这是通过张量投票实现的，张量投票最初设计用于从噪声输入中推断结构，此处被改造为在测试时优化过程中提供监督，从而获得更准确的场景几何。我们引入了一类新的三维损失函数，该损失不依赖于渲染，并且可以与文献中先前报告的所有损失函数相结合。当输入视图稀疏且由于图像监督有限而几何正则化至关重要时，我们的三维损失尤为有效。我们的方法易于与多种不同的骨干网络集成，在DTU和Tanks-and-Temples数据集上的实验表明，与骨干网络相比，TV-SGS改善了输出的几何结构，同时保持或提升了渲染质量。
英文摘要
Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to synthesize the pixels of novel views and are jointly optimized even though they only affect each other indirectly, via pixels they project to in common. We present an approach that enables direct communication among splats to enhance the geometric structures they form in 3D. This is accomplished by Tensor Voting, which was originally designed to infer structures from noisy inputs and has been adapted here to provide supervision during test-time optimization, leading to more accurate scene geometry. We introduce a new class of 3D losses that do not rely on rendering and can be combined with essentially all losses previously reported in the literature. Our 3D losses are especially effective when the input views are sparse and geometric regularization is essential due to limited supervision from the images. Our method is easy to integrate with a diverse set of backbones, and our experiments on the DTU and Tanks-and-Temples datasets demonstrate that TV-SGS improves the geometry of the outputs compared to the backbone, while maintaining or improving rendering quality.
