Gaussian Splatting has become a leading reconstruction technique, known for its high-quality novel view synthesis and detailed reconstruction. However, most existing methods require dense, calibrated views. Reconstructing from free sparse images often leads to poor surface due to limited overlap and overfitting. We introduce FSFSplatter, a new approach for fast surface reconstruction from free sparse images. Our method integrates end-to-end dense Gaussian initialization, camera parameter estimation, and geometry-enhanced scene optimization. Specifically, FSFSplatter employs a large Transformer to encode multi-view images and generates a dense and geometrically consistent Gaussian scene initialization via a self-splitting Gaussian head. It eliminates local floaters through contribution-based pruning and mitigates overfitting to limited views by leveraging depth and multi-view feature supervision with differentiable camera parameters during rapid optimization. FSFSplatter outperforms current state-of-the-art methods on widely used DTU, Replica, and BlendedMVS datasets.

翻译：高斯泼溅已成为领先的重构技术，以其高质量的新视角合成和精细重构而闻名。然而，大多数现有方法需要密集且已校准的视角。从自由稀疏图像进行重构常因重叠有限和过拟合而导致表面质量差。我们提出 FSFSplatter，一种从自由稀疏图像进行快速表面重构的新方法。该方法集成了端到端的稠密高斯初始化、相机参数估计和几何增强的场景优化。具体而言，FSFSplatter 使用大型 Transformer 编码多视角图像，并通过自分裂高斯头生成稠密且几何一致的高斯场景初始化。它通过基于贡献度的剪枝消除局部漂浮物，并在快速优化期间利用深度和多视角特征监督与可微相机参数，缓解对有限视角的过拟合。FSFSplatter 在广泛使用的 DTU、Replica 和 BlendedMVS 数据集上优于当前最先进的方法。