用稀疏图像2分钟内重建高质量表面并生成新视角
FSFSplatter: Build Surface and Novel Views with Sparse-Views within 2min
- 端到端初始化+相机参数估计+几何优化一体化
- 在DTU/Replica/BlendedMVS上超越现有最佳方法
- 适合快速3D重建且无密集拍摄条件的场景
高斯点阵已成为主流三维重建技术，以高质量的新视角合成和精细重建著称。然而，现有方法大多需要密集校准视图，从自由稀疏图像重建常因视图重叠少导致表面质量差且易过拟合。本文提出FSFSplatter，一种基于自由稀疏图像的快速表面重建方法。该方法集成端到端稠密高斯初始化、相机参数估计与几何增强优化。具体而言，采用大型Transformer编码多视图图像，通过自分裂高斯头生成稠密且几何一致的初始场景；利用贡献度剪枝消除局部噪声点，并通过深度与多视图特征监督结合可微相机参数，在快速优化中缓解对有限视图的过拟合问题。在广泛使用的DTU、Replica和BlendedMVS数据集上，该方法优于当前最先进水平。
原文摘要 · Abstract (English)
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.
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