Abstract

We present an efficient structure from motion algorithm that can deal with large image collections in a fraction of time and effort of previous approaches while providing comparable quality of the scene and camera reconstruction. First, we employ fast image indexing using large image vocabularies to measure visual overlap of images without running actual image matching. Then, we select a small subset from the set of input images by computing its approximate minimal connected dominat- ing set by a fast polynomial algorithm. Finally, we use task prioritization to avoid spending too much time in a few difficult matching problems instead of exploring other easier options. Thus we avoid wasting time on image pairs with low chance of success and avoid matching of highly re- dundant images of landmarks. We present results for several challenging sets of thousands of perspective as well as omnidirectional images.