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/.

翻译：本文提出Dense-SfM，一种新颖的运动恢复结构框架，旨在从多视角图像实现稠密且精确的三维重建。传统SfM方法通常依赖稀疏关键点匹配，这限制了重建的精度和点云密度，尤其是在纹理缺失区域。Dense-SfM通过将稠密匹配与基于高斯泼溅的轨迹扩展相结合来解决这一局限，从而生成更一致、更长的特征轨迹。为进一步提升重建精度，Dense-SfM配备了一个多视角核化匹配模块，该模块利用Transformer和高斯过程架构，实现跨多视角的鲁棒轨迹优化。在ETH3D和Texture-Poor SfM数据集上的评估表明，Dense-SfM在精度和密度方面相较于现有先进方法有显著提升。项目页面：https://icetea-cv.github.io/densesfm/。