A Constrained Radial Agglomerative Clustering Algorithm for Efficient Structure From Motion

Building three-dimensional models effectively and accurately is an important issue. In this letter, a new image set partition method for efficient structure from motion (SfM) from a set of unevenly distributed images is proposed. Given the largest connected component in the image matching graph, we first reconstruct a base model from a set of images with large overlap and sufficient feature correspondences. Then, a novel constrained radial agglomerative clustering algorithm is proposed to divide the remaining images, so that each image cluster could be independently added to the base model in parallel. Finally, all the partial models are merged into a complete scene. Experiment results show that the proposed method works better than the popular normalized-cuts-based SfM method.