用密集匹配提升三维重建精度，尤其适合无纹理场景。
Dense-SfM: Structure from Motion with Dense Consistent Matching
- 引入高斯点阵追踪扩展特征轨迹，实现更长更一致的匹配
- 在ETH3D和无纹理数据集上重建精度与点密度显著优于现有方法
- 适合需要高精度三维重建的工业或自动驾驶应用
我们提出Dense-SfM，一种新型多视角三维重建框架，旨在从多视图图像中实现密集且高精度的重建。传统SfM依赖稀疏关键点匹配，限制了重建精度与点密度，尤其在无纹理区域表现不佳。Dense-SfM通过结合密集匹配与基于高斯点阵（GS）的轨迹扩展机制，获得更一致、更长的特征轨迹。为进一步提升重建精度，该框架还引入了融合Transformer与高斯过程（Gaussian Process）的多视角核化匹配模块，实现跨视角的鲁棒轨迹优化。在ETH3D与无纹理SfM数据集上的评估表明，Dense-SfM在准确性和点密度方面均显著优于当前最优方法。
原文摘要 · Abstract (English)
We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitation by integrating dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. To further improve reconstruction accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging transformer and Gaussian Process architectures, for robust track refinement across multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods. Project page: https://icetea-cv.github.io/densesfm/.
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