With the proliferation of small aerial vehicles, acquiring close up aerial imagery for high quality reconstruction of complex scenes is gaining importance. We present an adaptive view planning method to collect such images in an automated fashion. We start by sampling a small set of views to build a coarse proxy to the scene. We then present (i)~a method that builds a view manifold for view selection, and (ii) an algorithm to select a sparse set of views. The vehicle then visits these viewpoints to cover the scene, and the procedure is repeated until reconstruction quality converges or a desired level of quality is achieved. The view manifold provides an effective efficiency/quality compromise between using the entire 6 degree of freedom pose space and using a single view hemisphere to select the views. Our results show that, in contrast to existing "explore and exploit" methods which collect only two sets of views, reconstruction quality can be drastically improved by adding a third set. They also indicate that three rounds of data collection is sufficient even for very complex scenes. We compare our algorithm to existing methods in three challenging scenes. We require each algorithm to select the same number of views. Our algorithm generates views which produce the least reconstruction error.