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.
我们提出了Dense-SfM，这是一种新颖的运动结构从图像（SfM）框架，旨在从多视图图像中进行密集且精确的3D重建。传统SfM方法常依赖的稀疏关键点匹配，限制了精度和点的密度，尤其是在无纹理区域。Dense-SfM通过结合密集匹配和基于高斯溅射（GS）的轨迹扩展，克服了这一限制，提供了更加一致和更长的特征轨迹。为了进一步提高重建精度，Dense-SfM配备了一个多视图核化匹配模块，利用变换器和高斯过程架构，在多视图之间进行稳健的轨迹优化。对ETH3D和Texture-Poor SfM数据集的评估表明，Dense-SfM在精度和密度方面相较于现有最先进的方法有了显著提升。