Efficient large-scale photometric reconstruction using Divide-Recon-Fuse 3D Structure from Motion

We propose an efficient framework for large-scale 3D reconstruction from a large set of photos following the Structure-from-Motion (SfM) paradigm with divide-conquer and fusion. Our main novelty is to ensure commonality from overlaps between image sets corresponding to their reconstructions, which facilitates effective stitching and fusion. Specifically, such commonality is ensured by selecting a set of duplicated images (which are termed anchor images) in adjacent image sets prior to the 3D reconstruction. The anchor images can assist accurate fusion of the 3D point clouds. We describe an efficient RANSAC scheme for pairwise stitching. Our method is intuitively scalable to large site reconstruction via subdivision and fusion following a graph construct. We further describe another RANSAC algorithm to improve loop closure in our anchor image approach. Experimental results on reconstructing a large portion of a university campus demonstrate the efficacy of our method.