TV-SGS: Gaussian Splatting with Geometric Information Propagation via Tensor Voting under sparse views

2026-09-07

AI Summary

本文提出了一种通过张量投票在稀疏视角下增强高斯点几何结构的方法，以改善场景几何重建，并引入新的3D损失函数来提高准确性。

Abstract

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.

Problem

• Gaussian Splatting
• novel view synthesis
• sparse views
• geometric structures
• Tensor Voting

Innovation

• Tensor Voting
• Gaussian Splatting
• 3D Losses
• Geometric Regularization