  Structure from Motion or the sparse 3D reconstruction out of individual
photos is a long studied topic in computer vision. Yet none of the existing
reconstruction pipelines fully addresses a progressive scenario where images
are only getting available during the reconstruction process and intermediate
results are delivered to the user. Incremental pipelines are capable of growing
a 3D model but often get stuck in local minima due to wrong (binding) decisions
taken based on incomplete information. Global pipelines on the other hand need
the access to the complete viewgraph and are not capable of delivering
intermediate results. In this paper we propose a new reconstruction pipeline
working in a progressive manner rather than in a batch processing scheme. The
pipeline is able to recover from failed reconstructions in early stages, avoids
to take binding decisions, delivers a progressive output and yet maintains the
capabilities of existing pipelines. We demonstrate and evaluate our method on
diverse challenging public and dedicated datasets including those with highly
symmetric structures and compare to the state of the art.
