无需真实相机位姿，仅用稀疏图像即可实现高质量3D重建。
No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
- 共享特征主干同步预测3D高斯点与相机位姿，一步完成推理。
- 在视角变化大、重叠少的情况下仍达顶尖渲染效果。
- 适合无位姿数据的实用场景，如移动设备或低资源环境。
我们提出SPFSplat，一种从稀疏多视图图像进行3D高斯点云拼贴的高效框架，训练和推理均无需真实相机位姿。该方法采用共享特征提取主干，能在单次前馈中从无位姿输入同时预测3D高斯原型和相机位姿（归一化空间）。除基于估计新视角位姿的渲染损失外，还引入重投影损失，强化像素对齐的高斯原型以增强几何约束。这种无位姿训练范式与高效的单步前馈设计使其非常适合实际应用。值得注意的是，尽管无位姿监督，SPFSplat在显著视角变化和有限图像重叠下仍实现顶尖的新视角合成性能，并超越近期使用几何先验的方法在相对位姿估计上的表现。代码与训练模型见项目主页：https://ranrhuang.github.io/spfsplat/。
原文摘要 · Abstract (English)
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/.
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