Efficient tree-structured SfM by RANSAC generalized Procrustes analysis

Abstract

This paper proposes a tree-structured structure-from-motion (SfM) method that recovers 3D scene structures and estimates camera poses from unordered image sets. Starting from atomic structures spanning the scene, we build well-connected structure groups, and propose RANSAC generalized Procrustes analysis (RGPA) to glue structures in the same group. The grouping-aligning operations hierarchically proceed until the full scene is reconstructed. Our work is the first attempt of using GPA for modern 3D reconstruction tasks. RGPA is able to merge multiple structures at a time and automatically identify outliers. The reconstruction tree is much more compact and balanced than previous hierarchical SfM methods and has a very shallow depth. These advantages, along with the resulting removal of intermediate bundle adjustments, lead to significantly improved computational efficiency over state-of-the-art SfM methods. The cameras and 3D scene can be robustly recovered in the presence of moderate noise. We verify the efficacy of our method on a variety of datasets, and demonstrate that our method is able to produce metric reconstructions efficiently and robustly.