G3Splat：几何一致的可推广高斯点渲染
G3Splat: Geometrically Consistent Generalizable Gaussian Splatting
AI总结：
研究3D高斯点渲染，指出多数方法仅用视图合成损失训练有局限。提出G3Splat框架，通过可微几何先验解决问题，提升高斯点几何保真度，兼顾多任务性能。
摘要：
3D高斯已成为实时点渲染和高质量新视图合成的强大场景表示。这推动了可推广点渲染方法的发展。然而，大多数此类方法主要通过视图合成损失进行监督。我们表明此学习目标约束不足。我们引入G3Splat，通过对预测的3D高斯使用可微几何先验来解决这些退化问题，使学习问题适定。
英文摘要：
3D Gaussians have become a powerful scene representation for real-time splatting and high-quality novel-view synthesis. This has motivated generalizable splatting -- methods that adapt feed-forward geometry prediction networks to produce per-pixel Gaussians from a set of images. However, most generalizable splatting pipelines are supervised primarily through a view-synthesis loss to predict Gaussian orientation, anisotropic scale, opacity, and appearance in addition to their locations. We show that this learning objective is under-constrained. Models trained with view synthesis alone produce splats whose orientations and scales have no geometric connotation. The result is that, while producing decent view-synthesis performance, nearly all generalizable splatting methods produce geometrically inaccurate and misaligned Gaussians. We introduce G3Splat, a geometry-consistent generalizable splatting framework that addresses these degeneracies through differentiable geometric priors on the predicted 3D Gaussians, making the learning problem well-posed. These priors encourage the per-pixel splats to remain on their viewing rays and to orient themselves in accordance with local surfaces. Our priors are architecture-agnostic and can be incorporated into any previously studied geometric backbone for generalizable splatting, as well as different scene representations. We test G3Splat with both DUSt3R-style and VGGT-style backbones to predict pixel-aligned full-rank 3DGS as well as surfel-like 2DGS. Trained on RE10K, G3Splat produces Gaussian splats with significantly higher geometric fidelity than baselines, providing state-of-the-art novel-view depth, mesh reconstruction, and relative pose estimation performance while preserving novel-view synthesis quality, as evaluated on datasets such as ACID and ScanNet. Code and pretrained models are released on our project page.
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。
