An Efficient Visual SfM Framework Using Planar Markers

Abstract

Structure-from-Motion (SfM) is a prevalent solution for vision measurement. Traditional SfM methods rely on sparse feature matching to establish visual associations between different views. However, both the incremental-based and global-based SfM show sensitiveness to outliers generated by existing hand-crafted features under challenging environments. Planar marker-assisted SfM is an alternative solution that employs artificial landmarks to help robust pose estimation. Nevertheless, using planar markers also exists the planar pose ambiguity problem, which means there may be more than one reasonable solution for pose estimation; it is a great challenge for camera pose estimation. This paper proposes an efficient planar marker-assisted global SfM method and resolves the planar pose ambiguity problem by examining the rotation consistency across multiple views. In particular, we construct two complementary graphs and present a graph filter method to filter out the views with pose ambiguity according to our designed rotation consistency checking. Afterward, we perform sparse feature matching following the proposed graph structure and make a pose-fixed bundle adjustment for high-quality 3D reconstruction. Extensive experiments have been conducted on synthetic and real datasets, and the results verify the effectiveness of the proposed method. Our method outperforms several state-of-the-art methods with encouraging efficiency, especially in challenging scenes.