Sorting unorganized photo sets for urban reconstruction

In spite of advanced acquisition technology, consumer cameras remain an attractive means for capturing 3D data. For reconstructing buildings it is easy to obtain large numbers of photos representing complete, all-around coverage of a building; however, such large photos collections are often unordered and unorganized, with unknown viewpoints. We present a method for reconstructing piecewise planar building models based on a near-linear time process that sorts such unorganized collections, quickly creating an image graph, an initial pose for each camera, and a piecewise-planar facade model. Our sorting technique first estimates single-view, piecewise planar geometry from each photo, then merges these single-view models together in an analysis phase that reasons about the global scene geometry. A key contribution of our technique is to perform this reasoning based on a number of typical constraints of buildings. This sorting process results in a piecewise planar model of the scene, a set of good initial camera poses, and a correspondence between photos. This information is useful in itself as an approximate scene model, but also represents a good initialization for structure from motion and multi-view stereo techniques from which refined models can be derived, at greatly reduced computational cost compared to prior techniques.