无姿态：从稀疏视图实现自监督的无姿态3D高斯点云渲染
No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
AI总结：
SPFSplat通过自监督方法实现无姿态的3D高斯点云渲染，无需真实姿态信息即可在稀疏视图下实现高质量的视点合成和姿态估计。
摘要：
我们介绍了SPFSplat，一种高效的从稀疏多视角图像进行3D高斯点云渲染的框架，其在训练和推理过程中均不需要真实姿态信息。它采用共享的特征提取主干网络，能够在单次前向传播步骤中，从无姿态输入中同时预测3D高斯基本体和相机姿态。除了基于估计的新视角姿态的渲染损失外，还集成了回投影损失，以强制学习对齐像素的高斯基本体，以增强几何约束。这种无姿态训练范式和高效的单步前向传播设计使SPFSplat非常适合实际应用。值得注意的是，尽管没有姿态监督，SPFSplat在显著视角变化和有限图像重叠的情况下，仍能实现最新视点合成的最先进性能。它还超越了最近使用几何先验训练的方法在相对姿态估计中的表现。代码和训练模型可在我们的项目页面上获得：https://ranrhuang.github.io/spfsplat/。
英文摘要：
We introduce SPFSplat, an efficient framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. It employs a shared feature extraction backbone, enabling simultaneous prediction of 3D Gaussian primitives and camera poses in a canonical space from unposed inputs within a single feed-forward step. Alongside the rendering loss based on estimated novel-view poses, a reprojection loss is integrated to enforce the learning of pixel-aligned Gaussian primitives for enhanced geometric constraints. This pose-free training paradigm and efficient one-step feed-forward design make SPFSplat well-suited for practical applications. Remarkably, despite the absence of pose supervision, SPFSplat achieves state-of-the-art performance in novel view synthesis even under significant viewpoint changes and limited image overlap. It also surpasses recent methods trained with geometry priors in relative pose estimation. Code and trained models are available on our project page: https://ranrhuang.github.io/spfsplat/.
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。
